2025-03-04T20:09:58.9082463Z Current runner version: '2.322.0' 2025-03-04T20:09:58.9088853Z Runner name: 'i-07188c9acbdc11b95' 2025-03-04T20:09:58.9089963Z Runner group name: 'Default' 2025-03-04T20:09:58.9090788Z Machine name: 'ip-10-0-30-106' 2025-03-04T20:09:58.9094271Z ##[group]GITHUB_TOKEN Permissions 2025-03-04T20:09:58.9097092Z Actions: read 2025-03-04T20:09:58.9097609Z Attestations: read 2025-03-04T20:09:58.9098101Z Checks: read 2025-03-04T20:09:58.9098621Z Contents: read 2025-03-04T20:09:58.9099097Z Deployments: read 2025-03-04T20:09:58.9099626Z Discussions: read 2025-03-04T20:09:58.9100105Z Issues: read 2025-03-04T20:09:58.9100570Z Metadata: read 2025-03-04T20:09:58.9101050Z Packages: read 2025-03-04T20:09:58.9101511Z Pages: read 2025-03-04T20:09:58.9101981Z PullRequests: read 2025-03-04T20:09:58.9102532Z RepositoryProjects: read 2025-03-04T20:09:58.9103072Z SecurityEvents: read 2025-03-04T20:09:58.9103572Z Statuses: read 2025-03-04T20:09:58.9104051Z ##[endgroup] 2025-03-04T20:09:58.9107030Z Secret source: Actions 2025-03-04T20:09:58.9107793Z Prepare workflow directory 2025-03-04T20:09:58.9481855Z Prepare all required actions 2025-03-04T20:09:58.9513537Z Getting action download info 2025-03-04T20:09:59.1370751Z Download action repository 'pytorch/test-infra@main' (SHA:79438512a0632583899938d3b0277da78f5569e0) 2025-03-04T20:10:00.6237597Z Download action repository 'pytorch/pytorch@main' (SHA:439395c0ae0234b529fd1b5ce30efca68be93f97) 2025-03-04T20:10:14.8995282Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0) 2025-03-04T20:10:15.1145385Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2025-03-04T20:10:15.3398686Z Getting action download info 2025-03-04T20:10:15.4595715Z Download action repository 'actions/checkout@v4' (SHA:11bd71901bbe5b1630ceea73d27597364c9af683) 2025-03-04T20:10:15.6627236Z Getting action download info 2025-03-04T20:10:15.7707655Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e) 2025-03-04T20:10:15.9255757Z Getting action download info 2025-03-04T20:10:16.0249333Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2025-03-04T20:10:16.1984240Z Getting action download info 2025-03-04T20:10:16.3241844Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/tags/ciflow/inductor/148205 (1b7498080987913ecb3aff6253c5e88f3540d911) 2025-03-04T20:10:16.3243991Z ##[group] Inputs 2025-03-04T20:10:16.3244312Z build-environment: linux-jammy-py3.9-gcc11-build 2025-03-04T20:10:16.3246247Z test-matrix: {"include": [{"config": "cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "inductor_torchbench_cpu_smoketest_perf", "shard": 1, "num_shards": 1, "runner": "linux.24xl.spr-metal"}]} 2025-03-04T20:10:16.3248311Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:10:16.3248866Z sync-tag: 2025-03-04T20:10:16.3249574Z timeout-minutes: 240 2025-03-04T20:10:16.3249788Z use-gha: 2025-03-04T20:10:16.3249992Z dashboard-tag: 2025-03-04T20:10:16.3250207Z s3-bucket: gha-artifacts 2025-03-04T20:10:16.3250444Z aws-role-to-assume: 2025-03-04T20:10:16.3251229Z disable-monitor: false 2025-03-04T20:10:16.3251549Z ##[endgroup] 2025-03-04T20:10:16.3251971Z Complete job name: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:10:16.3711492Z A job started hook has been configured by the self-hosted runner administrator 2025-03-04T20:10:16.3792823Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2025-03-04T20:10:16.3800074Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:10:16.3800521Z ##[endgroup] 2025-03-04T20:10:17.3376080Z Runner Type: linux.8xlarge.amx 2025-03-04T20:10:17.3376592Z Instance Type: m7i-flex.8xlarge 2025-03-04T20:10:17.3376854Z AMI Name: unknown 2025-03-04T20:10:17.3404379Z AMI ID: ami-05b10e08d247fb927 2025-03-04T20:10:21.7264338Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main 2025-03-04T20:10:21.7264775Z with: 2025-03-04T20:10:21.7265555Z github-secret: *** 2025-03-04T20:10:21.7267972Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-03-04T20:10:21.7268487Z activate-with-label: false 2025-03-04T20:10:21.7268707Z label: with-ssh 2025-03-04T20:10:21.7268899Z remove-existing-keys: true 2025-03-04T20:10:21.7269101Z fail-silently: true 2025-03-04T20:10:21.7269290Z env: 2025-03-04T20:10:21.7269463Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:10:21.7269667Z ##[endgroup] 2025-03-04T20:10:21.8254692Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2025-03-04T20:10:21.8257370Z ciflow reference detected, attempting to extract PR number 2025-03-04T20:10:22.1108194Z Grabbing public ssh keys from https://github.com/pytorch-bot[bot].keys 2025-03-04T20:10:22.1711802Z No SSH keys found for user pytorch-bot[bot] 2025-03-04T20:10:22.1712351Z Grabbing public ssh keys from https://github.com/williamwen42.keys 2025-03-04T20:10:22.2262845Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh 2025-03-04T20:10:22.2278985Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys 2025-03-04T20:10:22.2322612Z Login using: ssh ec2-user@ec2-3-237-96-114.compute-1.amazonaws.com 2025-03-04T20:10:22.2323124Z All testing is done inside the container, to start an interactive session run: 2025-03-04T20:10:22.2323490Z docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-03-04T20:10:22.2446354Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main 2025-03-04T20:10:22.2446686Z with: 2025-03-04T20:10:22.2446878Z no-sudo: true 2025-03-04T20:10:22.2447082Z submodules: recursive 2025-03-04T20:10:22.2447292Z fetch-depth: 0 2025-03-04T20:10:22.2447477Z env: 2025-03-04T20:10:22.2447660Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:10:22.2447871Z ##[endgroup] 2025-03-04T20:10:22.2517174Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T20:10:22.2517822Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T20:10:22.2525788Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:10:22.2526053Z env: 2025-03-04T20:10:22.2526239Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:10:22.2526443Z ##[endgroup] 2025-03-04T20:10:22.2613529Z ##[group]Run retry () { 2025-03-04T20:10:22.2613863Z retry () { 2025-03-04T20:10:22.2614118Z  $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*) 2025-03-04T20:10:22.2614514Z } 2025-03-04T20:10:22.2614725Z echo "${GITHUB_WORKSPACE}" 2025-03-04T20:10:22.2614988Z if [ -z "${NO_SUDO}" ]; then 2025-03-04T20:10:22.2615257Z  retry sudo rm -rf "${GITHUB_WORKSPACE}" 2025-03-04T20:10:22.2615505Z else 2025-03-04T20:10:22.2615729Z  retry rm -rf "${GITHUB_WORKSPACE}" 2025-03-04T20:10:22.2615961Z fi 2025-03-04T20:10:22.2616292Z mkdir "${GITHUB_WORKSPACE}" 2025-03-04T20:10:22.2616509Z  2025-03-04T20:10:22.2616795Z # Use all available CPUs for fetching 2025-03-04T20:10:22.2617056Z cd "${GITHUB_WORKSPACE}" 2025-03-04T20:10:22.2617297Z git config --global fetch.parallel 0 2025-03-04T20:10:22.2617563Z git config --global submodule.fetchJobs 0 2025-03-04T20:10:22.2622300Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:10:22.2622572Z env: 2025-03-04T20:10:22.2622754Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:10:22.2622956Z NO_SUDO: true 2025-03-04T20:10:22.2623135Z ##[endgroup] 2025-03-04T20:10:22.2643396Z /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-03-04T20:10:22.2751029Z ##[group]Run actions/checkout@v4 2025-03-04T20:10:22.2751294Z with: 2025-03-04T20:10:22.2751517Z ref: 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:10:22.2751779Z fetch-depth: 0 2025-03-04T20:10:22.2751982Z submodules: recursive 2025-03-04T20:10:22.2752232Z show-progress: false 2025-03-04T20:10:22.2752453Z repository: pytorch/pytorch 2025-03-04T20:10:22.2752750Z token: *** 2025-03-04T20:10:22.2752944Z ssh-strict: true 2025-03-04T20:10:22.2753138Z ssh-user: git 2025-03-04T20:10:22.2753343Z persist-credentials: true 2025-03-04T20:10:22.2753564Z clean: true 2025-03-04T20:10:22.2753764Z sparse-checkout-cone-mode: true 2025-03-04T20:10:22.2754015Z fetch-tags: false 2025-03-04T20:10:22.2754211Z lfs: false 2025-03-04T20:10:22.2754406Z set-safe-directory: true 2025-03-04T20:10:22.2754624Z env: 2025-03-04T20:10:22.2754812Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:10:22.2755025Z ##[endgroup] 2025-03-04T20:10:22.3760422Z Syncing repository: pytorch/pytorch 2025-03-04T20:10:22.3761604Z ##[group]Getting Git version info 2025-03-04T20:10:22.3761950Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-03-04T20:10:22.3762451Z [command]/usr/bin/git version 2025-03-04T20:10:22.3762671Z git version 2.47.1 2025-03-04T20:10:22.3771575Z ##[endgroup] 2025-03-04T20:10:22.3776018Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/2c8984ac-1f9c-4e68-b27f-348f395dba15/.gitconfig' 2025-03-04T20:10:22.3790547Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/2c8984ac-1f9c-4e68-b27f-348f395dba15' before making global git config changes 2025-03-04T20:10:22.3792973Z Adding repository directory to the temporary git global config as a safe directory 2025-03-04T20:10:22.3793523Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-03-04T20:10:22.3823196Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-03-04T20:10:22.3825840Z ##[group]Initializing the repository 2025-03-04T20:10:22.3831343Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-03-04T20:10:22.3861493Z hint: Using 'master' as the name for the initial branch. This default branch name 2025-03-04T20:10:22.3861967Z hint: is subject to change. To configure the initial branch name to use in all 2025-03-04T20:10:22.3862346Z hint: of your new repositories, which will suppress this warning, call: 2025-03-04T20:10:22.3862627Z hint: 2025-03-04T20:10:22.3862866Z hint: git config --global init.defaultBranch 2025-03-04T20:10:22.3863117Z hint: 2025-03-04T20:10:22.3863373Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2025-03-04T20:10:22.3863735Z hint: 'development'. The just-created branch can be renamed via this command: 2025-03-04T20:10:22.3864036Z hint: 2025-03-04T20:10:22.3864220Z hint: git branch -m 2025-03-04T20:10:22.3864895Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2025-03-04T20:10:22.3868691Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2025-03-04T20:10:22.3899672Z ##[endgroup] 2025-03-04T20:10:22.3900058Z ##[group]Disabling automatic garbage collection 2025-03-04T20:10:22.3902082Z [command]/usr/bin/git config --local gc.auto 0 2025-03-04T20:10:22.3931928Z ##[endgroup] 2025-03-04T20:10:22.3932272Z ##[group]Setting up auth 2025-03-04T20:10:22.3939830Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-03-04T20:10:22.3981002Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :" 2025-03-04T20:10:22.4375387Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-03-04T20:10:22.4405364Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :" 2025-03-04T20:10:22.4703518Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-03-04T20:10:22.4766838Z ##[endgroup] 2025-03-04T20:10:22.4767425Z ##[group]Fetching the repository 2025-03-04T20:10:22.4771977Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/* 2025-03-04T20:11:06.9823420Z From https://github.com/pytorch/pytorch 2025-03-04T20:11:06.9824072Z * [new branch] 2.1-dynamic-doc -> origin/2.1-dynamic-doc 2025-03-04T20:11:06.9824457Z * [new branch] 2.6.0.dev20241004+ -> origin/2.6.0.dev20241004+ 2025-03-04T20:11:06.9824851Z * [new branch] 20250219_e8m0_intermediate -> origin/20250219_e8m0_intermediate 2025-03-04T20:11:06.9825232Z * [new branch] 20250219_test -> origin/20250219_test 2025-03-04T20:11:06.9825782Z * [new branch] Adjust-Description-for-linux-binary-test-Workflow -> origin/Adjust-Description-for-linux-binary-test-Workflow 2025-03-04T20:11:06.9826345Z * [new branch] Chillee-patch-5 -> origin/Chillee-patch-5 2025-03-04T20:11:06.9826744Z * [new branch] Flamefire-patch-1 -> origin/Flamefire-patch-1 2025-03-04T20:11:06.9827164Z * [new branch] HDCharles-2.6.0-release-notes -> origin/HDCharles-2.6.0-release-notes 2025-03-04T20:11:06.9827633Z * [new branch] JackCaoG/add_new_lazy_counter_macro -> origin/JackCaoG/add_new_lazy_counter_macro 2025-03-04T20:11:06.9828462Z * [new branch] JackCaoG/dynamo_make_fx_non_core_aten_ops -> origin/JackCaoG/dynamo_make_fx_non_core_aten_ops 2025-03-04T20:11:06.9828962Z * [new branch] JackCaoG/fix_xla_torchbench -> origin/JackCaoG/fix_xla_torchbench 2025-03-04T20:11:06.9843211Z * [new branch] JackCaoG/update_dynamo_doc -> origin/JackCaoG/update_dynamo_doc 2025-03-04T20:11:06.9843866Z * [new branch] JackCaoG/update_xla_pin_to_skip_test -> origin/JackCaoG/update_xla_pin_to_skip_test 2025-03-04T20:11:06.9844422Z * [new branch] JackCaoG/update_xla_pin_to_skip_test2 -> origin/JackCaoG/update_xla_pin_to_skip_test2 2025-03-04T20:11:06.9844886Z * [new branch] NicolasHug-patch-2 -> origin/NicolasHug-patch-2 2025-03-04T20:11:06.9845282Z * [new branch] PR-AOTInductorNoneBug -> origin/PR-AOTInductorNoneBug 2025-03-04T20:11:06.9845679Z * [new branch] PR-AOTInductorNoneBugFix -> origin/PR-AOTInductorNoneBugFix 2025-03-04T20:11:06.9846051Z * [new branch] PR-FixConfigsIssue -> origin/PR-FixConfigsIssue 2025-03-04T20:11:06.9846408Z * [new branch] PR-NoneBugFix-viable -> origin/PR-NoneBugFix-viable 2025-03-04T20:11:06.9846752Z * [new branch] PR-ResetToZero -> origin/PR-ResetToZero 2025-03-04T20:11:06.9847124Z * [new branch] Remove-linux_t4g_2xlarge-Usage -> origin/Remove-linux_t4g_2xlarge-Usage 2025-03-04T20:11:06.9847499Z * [new branch] Revert-PR-110949 -> origin/Revert-PR-110949 2025-03-04T20:11:06.9848019Z * [new branch] Update-Flash-Packaging -> origin/Update-Flash-Packaging 2025-03-04T20:11:06.9848423Z * [new branch] Valentine/flash_attention_bf16 -> origin/Valentine/flash_attention_bf16 2025-03-04T20:11:06.9848824Z * [new branch] _tmp-orig/release/2.6 -> origin/_tmp-orig/release/2.6 2025-03-04T20:11:06.9849179Z * [new branch] _tmp-release/2.6 -> origin/_tmp-release/2.6 2025-03-04T20:11:06.9849556Z * [new branch] abock/onnx-1.15.0-validation -> origin/abock/onnx-1.15.0-validation 2025-03-04T20:11:06.9850029Z * [new branch] abock/ort-nightly==1.16.0.dev20230908001 -> origin/abock/ort-nightly==1.16.0.dev20230908001 2025-03-04T20:11:06.9850554Z * [new branch] add-android-build-workflow -> origin/add-android-build-workflow 2025-03-04T20:11:06.9850931Z * [new branch] add-assign -> origin/add-assign 2025-03-04T20:11:06.9851325Z * [new branch] add_broadcast_functional_collective -> origin/add_broadcast_functional_collective 2025-03-04T20:11:06.9851759Z * [new branch] add_from_group_doc_and_test -> origin/add_from_group_doc_and_test 2025-03-04T20:11:06.9894226Z * [new branch] add_mha_to_autocast_policy -> origin/add_mha_to_autocast_policy 2025-03-04T20:11:06.9895099Z * [new branch] add_non_parallel_model_comparison -> origin/add_non_parallel_model_comparison 2025-03-04T20:11:06.9895636Z * [new branch] add_test_to_show_view_gap -> origin/add_test_to_show_view_gap 2025-03-04T20:11:06.9896040Z * [new branch] add_windows_testing_back -> origin/add_windows_testing_back 2025-03-04T20:11:06.9896438Z * [new branch] addmm-heuristic -> origin/addmm-heuristic 2025-03-04T20:11:06.9896831Z * [new branch] addsimde -> origin/addsimde 2025-03-04T20:11:06.9897179Z * [new branch] adi/gemm_bf16f32 -> origin/adi/gemm_bf16f32 2025-03-04T20:11:06.9897587Z * [new branch] ah-globalfeedback-hook -> origin/ah-globalfeedback-hook 2025-03-04T20:11:06.9897987Z * [new branch] alanwaketan/pin2 -> origin/alanwaketan/pin2 2025-03-04T20:11:06.9898401Z * [new branch] albanD-patch-1 -> origin/albanD-patch-1 2025-03-04T20:11:06.9899026Z * [new branch] albanD-patch-2 -> origin/albanD-patch-2 2025-03-04T20:11:06.9899391Z * [new branch] alt-disable -> origin/alt-disable 2025-03-04T20:11:06.9899747Z * [new branch] angelayi/144772 -> origin/angelayi/144772 2025-03-04T20:11:06.9900182Z * [new branch] angelayi/aot_inductor_bench_comp_time -> origin/angelayi/aot_inductor_bench_comp_time 2025-03-04T20:11:06.9900661Z * [new branch] angelayi/aot_inductor_benchmark -> origin/angelayi/aot_inductor_benchmark 2025-03-04T20:11:06.9901088Z * [new branch] angelayi/aot_inductor_torch -> origin/angelayi/aot_inductor_torch 2025-03-04T20:11:06.9901511Z * [new branch] angelayi/aoti_additional_files -> origin/angelayi/aoti_additional_files 2025-03-04T20:11:06.9901929Z * [new 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2025-03-04T20:11:07.3647389Z * [new tag] ciflow/binaries_wheel/143388 -> ciflow/binaries_wheel/143388 2025-03-04T20:11:07.3648548Z * [new tag] ciflow/binaries_wheel/144049 -> ciflow/binaries_wheel/144049 2025-03-04T20:11:07.3649167Z * [new tag] ciflow/binaries_wheel/146055 -> ciflow/binaries_wheel/146055 2025-03-04T20:11:07.3649344Z * [new tag] ciflow/binaries_wheel/146573 -> ciflow/binaries_wheel/146573 2025-03-04T20:11:07.3649758Z * [new tag] ciflow/binaries_wheel/147074 -> ciflow/binaries_wheel/147074 2025-03-04T20:11:07.3649939Z * [new tag] ciflow/binaries_wheel/147448 -> ciflow/binaries_wheel/147448 2025-03-04T20:11:07.3650421Z * [new tag] ciflow/binaries_wheel/147455 -> ciflow/binaries_wheel/147455 2025-03-04T20:11:07.3651027Z * [new tag] ciflow/binaries_wheel/148313 -> ciflow/binaries_wheel/148313 2025-03-04T20:11:07.3651644Z * [new tag] ciflow/binaries_wheel/148319 -> ciflow/binaries_wheel/148319 2025-03-04T20:11:07.3651842Z * [new tag] ciflow/cuda/70978 -> ciflow/cuda/70978 2025-03-04T20:11:07.3652230Z * [new tag] ciflow/cuda/70979 -> ciflow/cuda/70979 2025-03-04T20:11:07.3652593Z * [new tag] ciflow/cuda/70989 -> ciflow/cuda/70989 2025-03-04T20:11:07.3653551Z * [new tag] ciflow/inductor-cu126/140793 -> ciflow/inductor-cu126/140793 2025-03-04T20:11:07.3654045Z * [new tag] ciflow/inductor-micro-benchmark/141910 -> ciflow/inductor-micro-benchmark/141910 2025-03-04T20:11:07.3654422Z * [new tag] ciflow/inductor-perf-compare/140195 -> ciflow/inductor-perf-compare/140195 2025-03-04T20:11:07.3654966Z * [new tag] ciflow/inductor-perf-test-nightly/140195 -> ciflow/inductor-perf-test-nightly/140195 2025-03-04T20:11:07.3655443Z * [new tag] ciflow/inductor-periodic/140793 -> ciflow/inductor-periodic/140793 2025-03-04T20:11:07.3655954Z * [new tag] ciflow/inductor-periodic/145612 -> ciflow/inductor-periodic/145612 2025-03-04T20:11:07.3656432Z * [new tag] ciflow/inductor-periodic/147315 -> ciflow/inductor-periodic/147315 2025-03-04T20:11:07.3657700Z * [new tag] ciflow/inductor-rocm/140989 -> ciflow/inductor-rocm/140989 2025-03-04T20:11:07.3657856Z * [new tag] ciflow/inductor-rocm/141309 -> ciflow/inductor-rocm/141309 2025-03-04T20:11:07.3658029Z * [new tag] ciflow/inductor-rocm/141355 -> ciflow/inductor-rocm/141355 2025-03-04T20:11:07.3658485Z * [new tag] ciflow/inductor-rocm/146264 -> ciflow/inductor-rocm/146264 2025-03-04T20:11:07.3660412Z * [new tag] ciflow/inductor-rocm/146903 -> ciflow/inductor-rocm/146903 2025-03-04T20:11:07.3660615Z * [new tag] ciflow/inductor-rocm/147315 -> ciflow/inductor-rocm/147315 2025-03-04T20:11:07.3660776Z * [new tag] ciflow/inductor-rocm/147320 -> ciflow/inductor-rocm/147320 2025-03-04T20:11:07.3660931Z * [new tag] ciflow/inductor-rocm/147452 -> ciflow/inductor-rocm/147452 2025-03-04T20:11:07.3661262Z * [new tag] ciflow/inductor-rocm/147583 -> ciflow/inductor-rocm/147583 2025-03-04T20:11:07.3661647Z * [new tag] ciflow/inductor-rocm/147619 -> ciflow/inductor-rocm/147619 2025-03-04T20:11:07.3662680Z * [new tag] ciflow/inductor-rocm/148305 -> ciflow/inductor-rocm/148305 2025-03-04T20:11:07.3662846Z * [new tag] ciflow/inductor-rocm/148437 -> ciflow/inductor-rocm/148437 2025-03-04T20:11:07.3663496Z * [new tag] ciflow/inductor/110155 -> ciflow/inductor/110155 2025-03-04T20:11:07.3663790Z * [new tag] ciflow/inductor/113257 -> ciflow/inductor/113257 2025-03-04T20:11:07.3664154Z * [new tag] ciflow/inductor/119496 -> ciflow/inductor/119496 2025-03-04T20:11:07.3664453Z * [new tag] ciflow/inductor/119977 -> ciflow/inductor/119977 2025-03-04T20:11:07.3664897Z * [new tag] ciflow/inductor/120076 -> ciflow/inductor/120076 2025-03-04T20:11:07.3665286Z * [new tag] ciflow/inductor/121445 -> ciflow/inductor/121445 2025-03-04T20:11:07.3665692Z * [new tag] ciflow/inductor/124490 -> ciflow/inductor/124490 2025-03-04T20:11:07.3665966Z * [new tag] ciflow/inductor/125270 -> ciflow/inductor/125270 2025-03-04T20:11:07.3666417Z * [new tag] ciflow/inductor/125326 -> ciflow/inductor/125326 2025-03-04T20:11:07.3666679Z * [new tag] ciflow/inductor/125428 -> ciflow/inductor/125428 2025-03-04T20:11:07.3667212Z * [new tag] ciflow/inductor/125469 -> ciflow/inductor/125469 2025-03-04T20:11:07.3667757Z * [new tag] ciflow/inductor/125806 -> ciflow/inductor/125806 2025-03-04T20:11:07.3668539Z * [new tag] ciflow/inductor/125888 -> ciflow/inductor/125888 2025-03-04T20:11:07.3668947Z * [new tag] ciflow/inductor/125995 -> ciflow/inductor/125995 2025-03-04T20:11:07.3669349Z * [new tag] ciflow/inductor/126348 -> ciflow/inductor/126348 2025-03-04T20:11:07.3669717Z * [new tag] ciflow/inductor/127011 -> ciflow/inductor/127011 2025-03-04T20:11:07.3670233Z * [new tag] ciflow/inductor/127171 -> ciflow/inductor/127171 2025-03-04T20:11:07.3670549Z * [new tag] ciflow/inductor/127293 -> ciflow/inductor/127293 2025-03-04T20:11:07.3671012Z * [new tag] ciflow/inductor/127294 -> ciflow/inductor/127294 2025-03-04T20:11:07.3671495Z * [new tag] ciflow/inductor/129352 -> ciflow/inductor/129352 2025-03-04T20:11:07.3671841Z * [new tag] ciflow/inductor/129420 -> ciflow/inductor/129420 2025-03-04T20:11:07.3672249Z * [new tag] ciflow/inductor/130141 -> ciflow/inductor/130141 2025-03-04T20:11:07.3674246Z * [new tag] ciflow/inductor/130499 -> ciflow/inductor/130499 2025-03-04T20:11:07.3674422Z * [new tag] ciflow/inductor/130887 -> ciflow/inductor/130887 2025-03-04T20:11:07.3674557Z * [new tag] ciflow/inductor/131354 -> ciflow/inductor/131354 2025-03-04T20:11:07.3674691Z * [new tag] ciflow/inductor/132021 -> ciflow/inductor/132021 2025-03-04T20:11:07.3674845Z * [new tag] ciflow/inductor/132414 -> ciflow/inductor/132414 2025-03-04T20:11:07.3674990Z * [new tag] ciflow/inductor/133044 -> ciflow/inductor/133044 2025-03-04T20:11:07.3675539Z * [new tag] ciflow/inductor/133121 -> ciflow/inductor/133121 2025-03-04T20:11:07.3675986Z * [new tag] ciflow/inductor/133287 -> ciflow/inductor/133287 2025-03-04T20:11:07.3676144Z * [new tag] ciflow/inductor/133289 -> ciflow/inductor/133289 2025-03-04T20:11:07.3676442Z * [new tag] ciflow/inductor/133296 -> ciflow/inductor/133296 2025-03-04T20:11:07.3676882Z * [new tag] ciflow/inductor/133297 -> ciflow/inductor/133297 2025-03-04T20:11:07.3677323Z * [new tag] ciflow/inductor/133315 -> ciflow/inductor/133315 2025-03-04T20:11:07.3677800Z * [new tag] ciflow/inductor/133392 -> ciflow/inductor/133392 2025-03-04T20:11:07.3678102Z * [new tag] ciflow/inductor/133419 -> ciflow/inductor/133419 2025-03-04T20:11:07.3678597Z * [new tag] ciflow/inductor/133423 -> ciflow/inductor/133423 2025-03-04T20:11:07.3678845Z * [new tag] ciflow/inductor/133667 -> ciflow/inductor/133667 2025-03-04T20:11:07.3679364Z * [new tag] ciflow/inductor/133753 -> ciflow/inductor/133753 2025-03-04T20:11:07.3679672Z * [new tag] ciflow/inductor/134681 -> ciflow/inductor/134681 2025-03-04T20:11:07.3680143Z * [new tag] ciflow/inductor/135708 -> ciflow/inductor/135708 2025-03-04T20:11:07.3680655Z * [new tag] ciflow/inductor/135792 -> ciflow/inductor/135792 2025-03-04T20:11:07.3681526Z * [new tag] ciflow/inductor/136355 -> ciflow/inductor/136355 2025-03-04T20:11:07.3681673Z * [new tag] ciflow/inductor/136702 -> ciflow/inductor/136702 2025-03-04T20:11:07.3682125Z * [new tag] ciflow/inductor/137400 -> ciflow/inductor/137400 2025-03-04T20:11:07.3682353Z * [new tag] ciflow/inductor/137568 -> ciflow/inductor/137568 2025-03-04T20:11:07.3682767Z * [new tag] ciflow/inductor/137583 -> ciflow/inductor/137583 2025-03-04T20:11:07.3683276Z * [new tag] ciflow/inductor/137846 -> ciflow/inductor/137846 2025-03-04T20:11:07.3683683Z * [new tag] ciflow/inductor/137884 -> ciflow/inductor/137884 2025-03-04T20:11:07.3684077Z * [new tag] ciflow/inductor/138185 -> ciflow/inductor/138185 2025-03-04T20:11:07.3684514Z * [new tag] ciflow/inductor/138202 -> ciflow/inductor/138202 2025-03-04T20:11:07.3684847Z * [new tag] ciflow/inductor/138213 -> ciflow/inductor/138213 2025-03-04T20:11:07.3685107Z * [new tag] ciflow/inductor/138214 -> ciflow/inductor/138214 2025-03-04T20:11:07.3686121Z * [new tag] ciflow/inductor/138388 -> ciflow/inductor/138388 2025-03-04T20:11:07.3703133Z * [new tag] ciflow/inductor/138513 -> ciflow/inductor/138513 2025-03-04T20:11:07.3703629Z * [new tag] ciflow/inductor/138519 -> ciflow/inductor/138519 2025-03-04T20:11:07.3703823Z * [new tag] ciflow/inductor/138555 -> ciflow/inductor/138555 2025-03-04T20:11:07.3703960Z * [new tag] ciflow/inductor/138626 -> ciflow/inductor/138626 2025-03-04T20:11:07.3704099Z * [new tag] ciflow/inductor/138889 -> ciflow/inductor/138889 2025-03-04T20:11:07.3704229Z * [new tag] ciflow/inductor/138930 -> ciflow/inductor/138930 2025-03-04T20:11:07.3704366Z * [new tag] ciflow/inductor/139094 -> ciflow/inductor/139094 2025-03-04T20:11:07.3704495Z * [new tag] ciflow/inductor/139271 -> ciflow/inductor/139271 2025-03-04T20:11:07.3704642Z * [new tag] ciflow/inductor/139561 -> ciflow/inductor/139561 2025-03-04T20:11:07.3704771Z * [new tag] ciflow/inductor/139975 -> ciflow/inductor/139975 2025-03-04T20:11:07.3704907Z * [new tag] ciflow/inductor/140032 -> ciflow/inductor/140032 2025-03-04T20:11:07.3705220Z * [new tag] ciflow/inductor/140084 -> ciflow/inductor/140084 2025-03-04T20:11:07.3705359Z * [new tag] ciflow/inductor/140159 -> ciflow/inductor/140159 2025-03-04T20:11:07.3705487Z * [new tag] ciflow/inductor/140195 -> ciflow/inductor/140195 2025-03-04T20:11:07.3705623Z * [new tag] ciflow/inductor/140746 -> ciflow/inductor/140746 2025-03-04T20:11:07.3705750Z * [new tag] ciflow/inductor/140756 -> ciflow/inductor/140756 2025-03-04T20:11:07.3705885Z * [new tag] ciflow/inductor/140979 -> ciflow/inductor/140979 2025-03-04T20:11:07.3706016Z * [new tag] ciflow/inductor/141082 -> ciflow/inductor/141082 2025-03-04T20:11:07.3706151Z * [new tag] ciflow/inductor/141096 -> ciflow/inductor/141096 2025-03-04T20:11:07.3706279Z * [new tag] ciflow/inductor/141097 -> ciflow/inductor/141097 2025-03-04T20:11:07.3706416Z * [new tag] ciflow/inductor/141213 -> ciflow/inductor/141213 2025-03-04T20:11:07.3706543Z * [new tag] ciflow/inductor/141309 -> ciflow/inductor/141309 2025-03-04T20:11:07.3706677Z * [new tag] ciflow/inductor/141393 -> ciflow/inductor/141393 2025-03-04T20:11:07.3706804Z * [new tag] ciflow/inductor/141641 -> ciflow/inductor/141641 2025-03-04T20:11:07.3706940Z * [new tag] ciflow/inductor/141684 -> ciflow/inductor/141684 2025-03-04T20:11:07.3707068Z * [new tag] ciflow/inductor/141700 -> ciflow/inductor/141700 2025-03-04T20:11:07.3707202Z * [new tag] ciflow/inductor/141730 -> ciflow/inductor/141730 2025-03-04T20:11:07.3707409Z * [new tag] ciflow/inductor/141842 -> ciflow/inductor/141842 2025-03-04T20:11:07.3707544Z * [new tag] ciflow/inductor/141889 -> ciflow/inductor/141889 2025-03-04T20:11:07.3707745Z * [new tag] ciflow/inductor/141940 -> ciflow/inductor/141940 2025-03-04T20:11:07.3707923Z * [new tag] ciflow/inductor/141944 -> ciflow/inductor/141944 2025-03-04T20:11:07.3708074Z * [new tag] ciflow/inductor/141961 -> ciflow/inductor/141961 2025-03-04T20:11:07.3708211Z * [new tag] ciflow/inductor/142091 -> ciflow/inductor/142091 2025-03-04T20:11:07.3708337Z * [new tag] ciflow/inductor/142092 -> ciflow/inductor/142092 2025-03-04T20:11:07.3708472Z * [new tag] ciflow/inductor/142163 -> ciflow/inductor/142163 2025-03-04T20:11:07.3708611Z * [new tag] ciflow/inductor/142272 -> ciflow/inductor/142272 2025-03-04T20:11:07.3708748Z * [new tag] ciflow/inductor/142273 -> ciflow/inductor/142273 2025-03-04T20:11:07.3708873Z * [new tag] ciflow/inductor/142295 -> ciflow/inductor/142295 2025-03-04T20:11:07.3709005Z * [new tag] ciflow/inductor/142296 -> ciflow/inductor/142296 2025-03-04T20:11:07.3709134Z * [new tag] ciflow/inductor/142309 -> ciflow/inductor/142309 2025-03-04T20:11:07.3709269Z * [new tag] ciflow/inductor/142350 -> ciflow/inductor/142350 2025-03-04T20:11:07.3709397Z * [new tag] ciflow/inductor/142372 -> ciflow/inductor/142372 2025-03-04T20:11:07.3709530Z * [new tag] ciflow/inductor/142483 -> ciflow/inductor/142483 2025-03-04T20:11:07.3709663Z * [new tag] ciflow/inductor/142851 -> ciflow/inductor/142851 2025-03-04T20:11:07.3709790Z * [new tag] ciflow/inductor/143044 -> ciflow/inductor/143044 2025-03-04T20:11:07.3709928Z * [new tag] ciflow/inductor/143103 -> ciflow/inductor/143103 2025-03-04T20:11:07.3710064Z * [new tag] ciflow/inductor/143220 -> ciflow/inductor/143220 2025-03-04T20:11:07.3710199Z * [new tag] ciflow/inductor/143256 -> ciflow/inductor/143256 2025-03-04T20:11:07.3710366Z * [new tag] ciflow/inductor/143275 -> ciflow/inductor/143275 2025-03-04T20:11:07.3710506Z * [new tag] ciflow/inductor/143313 -> ciflow/inductor/143313 2025-03-04T20:11:07.3710642Z * [new tag] ciflow/inductor/143411 -> ciflow/inductor/143411 2025-03-04T20:11:07.3710975Z * [new tag] ciflow/inductor/143457 -> ciflow/inductor/143457 2025-03-04T20:11:07.3713801Z * [new tag] ciflow/inductor/143464 -> ciflow/inductor/143464 2025-03-04T20:11:07.3713985Z * [new tag] ciflow/inductor/143475 -> ciflow/inductor/143475 2025-03-04T20:11:07.3714139Z * [new tag] ciflow/inductor/143525 -> ciflow/inductor/143525 2025-03-04T20:11:07.3714289Z * [new tag] ciflow/inductor/143527 -> ciflow/inductor/143527 2025-03-04T20:11:07.3714586Z * [new tag] ciflow/inductor/143533 -> ciflow/inductor/143533 2025-03-04T20:11:07.3714820Z * [new tag] ciflow/inductor/143534 -> ciflow/inductor/143534 2025-03-04T20:11:07.3715057Z * [new tag] ciflow/inductor/143544 -> ciflow/inductor/143544 2025-03-04T20:11:07.3716193Z * [new tag] ciflow/inductor/143666 -> ciflow/inductor/143666 2025-03-04T20:11:07.3716524Z * [new tag] ciflow/inductor/143671 -> ciflow/inductor/143671 2025-03-04T20:11:07.3716692Z * [new tag] ciflow/inductor/143712 -> ciflow/inductor/143712 2025-03-04T20:11:07.3716935Z * [new tag] ciflow/inductor/143812 -> ciflow/inductor/143812 2025-03-04T20:11:07.3717278Z * [new tag] ciflow/inductor/143833 -> ciflow/inductor/143833 2025-03-04T20:11:07.3717681Z * [new tag] ciflow/inductor/143961 -> ciflow/inductor/143961 2025-03-04T20:11:07.3718219Z * [new tag] ciflow/inductor/143987 -> ciflow/inductor/143987 2025-03-04T20:11:07.3718861Z * [new tag] ciflow/inductor/144008 -> ciflow/inductor/144008 2025-03-04T20:11:07.3719323Z * [new tag] ciflow/inductor/144017 -> ciflow/inductor/144017 2025-03-04T20:11:07.3719563Z * [new tag] ciflow/inductor/144073 -> ciflow/inductor/144073 2025-03-04T20:11:07.3719882Z * [new tag] ciflow/inductor/144097 -> ciflow/inductor/144097 2025-03-04T20:11:07.3720291Z * [new tag] ciflow/inductor/144120 -> ciflow/inductor/144120 2025-03-04T20:11:07.3722406Z * [new tag] ciflow/inductor/144172 -> ciflow/inductor/144172 2025-03-04T20:11:07.3722706Z * [new tag] ciflow/inductor/144234 -> ciflow/inductor/144234 2025-03-04T20:11:07.3722874Z * [new tag] ciflow/inductor/144272 -> ciflow/inductor/144272 2025-03-04T20:11:07.3723005Z * [new tag] ciflow/inductor/144288 -> ciflow/inductor/144288 2025-03-04T20:11:07.3723419Z * [new tag] ciflow/inductor/144293 -> ciflow/inductor/144293 2025-03-04T20:11:07.3723557Z * [new tag] ciflow/inductor/144294 -> ciflow/inductor/144294 2025-03-04T20:11:07.3723912Z * [new tag] ciflow/inductor/144332 -> ciflow/inductor/144332 2025-03-04T20:11:07.3724237Z * [new tag] ciflow/inductor/144333 -> ciflow/inductor/144333 2025-03-04T20:11:07.3724957Z * [new tag] ciflow/inductor/144349 -> ciflow/inductor/144349 2025-03-04T20:11:07.3725139Z * [new tag] ciflow/inductor/144353 -> ciflow/inductor/144353 2025-03-04T20:11:07.3725529Z * [new tag] ciflow/inductor/144365 -> ciflow/inductor/144365 2025-03-04T20:11:07.3726020Z * [new tag] ciflow/inductor/144366 -> ciflow/inductor/144366 2025-03-04T20:11:07.3726354Z * [new tag] ciflow/inductor/144405 -> ciflow/inductor/144405 2025-03-04T20:11:07.3727243Z * [new tag] ciflow/inductor/144413 -> ciflow/inductor/144413 2025-03-04T20:11:07.3727538Z * [new tag] ciflow/inductor/144414 -> ciflow/inductor/144414 2025-03-04T20:11:07.3727833Z * [new tag] ciflow/inductor/144438 -> ciflow/inductor/144438 2025-03-04T20:11:07.3728242Z * [new tag] ciflow/inductor/144452 -> ciflow/inductor/144452 2025-03-04T20:11:07.3728405Z * [new tag] ciflow/inductor/144458 -> ciflow/inductor/144458 2025-03-04T20:11:07.3728676Z * [new tag] ciflow/inductor/144501 -> ciflow/inductor/144501 2025-03-04T20:11:07.3729027Z * [new tag] ciflow/inductor/144505 -> ciflow/inductor/144505 2025-03-04T20:11:07.3729440Z * [new tag] ciflow/inductor/144507 -> ciflow/inductor/144507 2025-03-04T20:11:07.3729892Z * [new tag] ciflow/inductor/144516 -> ciflow/inductor/144516 2025-03-04T20:11:07.3730219Z * [new tag] ciflow/inductor/144542 -> ciflow/inductor/144542 2025-03-04T20:11:07.3730634Z * [new tag] ciflow/inductor/144548 -> ciflow/inductor/144548 2025-03-04T20:11:07.3731036Z * [new tag] ciflow/inductor/144551 -> ciflow/inductor/144551 2025-03-04T20:11:07.3731453Z * [new tag] ciflow/inductor/144553 -> ciflow/inductor/144553 2025-03-04T20:11:07.3732029Z * [new tag] ciflow/inductor/144555 -> ciflow/inductor/144555 2025-03-04T20:11:07.3732255Z * [new tag] ciflow/inductor/144556 -> ciflow/inductor/144556 2025-03-04T20:11:07.3732954Z * [new tag] ciflow/inductor/144579 -> ciflow/inductor/144579 2025-03-04T20:11:07.3733303Z * [new tag] ciflow/inductor/144598 -> ciflow/inductor/144598 2025-03-04T20:11:07.3733600Z * [new tag] ciflow/inductor/144712 -> ciflow/inductor/144712 2025-03-04T20:11:07.3734034Z * [new tag] ciflow/inductor/144721 -> ciflow/inductor/144721 2025-03-04T20:11:07.3736401Z * [new tag] ciflow/inductor/144724 -> ciflow/inductor/144724 2025-03-04T20:11:07.3736734Z * [new tag] ciflow/inductor/144733 -> ciflow/inductor/144733 2025-03-04T20:11:07.3736890Z * [new tag] ciflow/inductor/144741 -> ciflow/inductor/144741 2025-03-04T20:11:07.3737115Z * [new tag] ciflow/inductor/144765 -> ciflow/inductor/144765 2025-03-04T20:11:07.3737244Z * [new tag] ciflow/inductor/144771 -> ciflow/inductor/144771 2025-03-04T20:11:07.3737487Z * [new tag] ciflow/inductor/144880 -> ciflow/inductor/144880 2025-03-04T20:11:07.3737650Z * [new tag] ciflow/inductor/144905 -> ciflow/inductor/144905 2025-03-04T20:11:07.3737789Z * [new tag] ciflow/inductor/144925 -> ciflow/inductor/144925 2025-03-04T20:11:07.3738245Z * [new tag] ciflow/inductor/144943 -> ciflow/inductor/144943 2025-03-04T20:11:07.3739678Z * [new tag] ciflow/inductor/144953 -> ciflow/inductor/144953 2025-03-04T20:11:07.3739989Z * [new tag] ciflow/inductor/144975 -> ciflow/inductor/144975 2025-03-04T20:11:07.3740208Z * [new tag] ciflow/inductor/144979 -> ciflow/inductor/144979 2025-03-04T20:11:07.3740480Z * [new tag] ciflow/inductor/144986 -> ciflow/inductor/144986 2025-03-04T20:11:07.3740968Z * [new tag] ciflow/inductor/144992 -> ciflow/inductor/144992 2025-03-04T20:11:07.3741380Z * [new tag] ciflow/inductor/145024 -> ciflow/inductor/145024 2025-03-04T20:11:07.3741777Z * [new tag] ciflow/inductor/145061 -> ciflow/inductor/145061 2025-03-04T20:11:07.3742201Z * [new tag] ciflow/inductor/145117 -> ciflow/inductor/145117 2025-03-04T20:11:07.3742635Z * [new tag] ciflow/inductor/145119 -> ciflow/inductor/145119 2025-03-04T20:11:07.3743483Z * [new tag] ciflow/inductor/145150 -> ciflow/inductor/145150 2025-03-04T20:11:07.3743703Z * [new tag] ciflow/inductor/145153 -> ciflow/inductor/145153 2025-03-04T20:11:07.3743923Z * [new tag] ciflow/inductor/145254 -> ciflow/inductor/145254 2025-03-04T20:11:07.3744349Z * [new tag] ciflow/inductor/145331 -> ciflow/inductor/145331 2025-03-04T20:11:07.3744768Z * [new tag] ciflow/inductor/145353 -> ciflow/inductor/145353 2025-03-04T20:11:07.3745177Z * [new tag] ciflow/inductor/145475 -> ciflow/inductor/145475 2025-03-04T20:11:07.3745616Z * [new tag] ciflow/inductor/145523 -> ciflow/inductor/145523 2025-03-04T20:11:07.3746289Z * [new tag] ciflow/inductor/145540 -> ciflow/inductor/145540 2025-03-04T20:11:07.3746484Z * [new tag] ciflow/inductor/145559 -> ciflow/inductor/145559 2025-03-04T20:11:07.3746780Z * [new tag] ciflow/inductor/145562 -> ciflow/inductor/145562 2025-03-04T20:11:07.3747758Z * [new tag] ciflow/inductor/145594 -> ciflow/inductor/145594 2025-03-04T20:11:07.3747961Z * [new tag] ciflow/inductor/145595 -> ciflow/inductor/145595 2025-03-04T20:11:07.3748103Z * [new tag] ciflow/inductor/145605 -> ciflow/inductor/145605 2025-03-04T20:11:07.3748436Z * [new tag] ciflow/inductor/145612 -> ciflow/inductor/145612 2025-03-04T20:11:07.3748883Z * [new tag] ciflow/inductor/145636 -> ciflow/inductor/145636 2025-03-04T20:11:07.3749290Z * [new tag] ciflow/inductor/145647 -> ciflow/inductor/145647 2025-03-04T20:11:07.3749652Z * [new tag] ciflow/inductor/145681 -> ciflow/inductor/145681 2025-03-04T20:11:07.3750406Z * [new tag] ciflow/inductor/145865 -> ciflow/inductor/145865 2025-03-04T20:11:07.3750636Z * [new tag] ciflow/inductor/145885 -> ciflow/inductor/145885 2025-03-04T20:11:07.3750868Z * [new tag] ciflow/inductor/145911 -> ciflow/inductor/145911 2025-03-04T20:11:07.3751496Z * [new tag] ciflow/inductor/145922 -> ciflow/inductor/145922 2025-03-04T20:11:07.3751659Z * [new tag] ciflow/inductor/145936 -> ciflow/inductor/145936 2025-03-04T20:11:07.3754914Z * [new tag] ciflow/inductor/145966 -> ciflow/inductor/145966 2025-03-04T20:11:07.3755229Z * [new tag] ciflow/inductor/145969 -> ciflow/inductor/145969 2025-03-04T20:11:07.3755465Z * [new tag] ciflow/inductor/145979 -> ciflow/inductor/145979 2025-03-04T20:11:07.3755659Z * [new tag] ciflow/inductor/145992 -> ciflow/inductor/145992 2025-03-04T20:11:07.3755909Z * [new tag] ciflow/inductor/146051 -> ciflow/inductor/146051 2025-03-04T20:11:07.3756606Z * [new tag] ciflow/inductor/146063 -> ciflow/inductor/146063 2025-03-04T20:11:07.3756897Z * [new tag] ciflow/inductor/146101 -> ciflow/inductor/146101 2025-03-04T20:11:07.3757111Z * [new tag] ciflow/inductor/146115 -> ciflow/inductor/146115 2025-03-04T20:11:07.3757260Z * [new tag] ciflow/inductor/146135 -> ciflow/inductor/146135 2025-03-04T20:11:07.3757494Z * [new tag] ciflow/inductor/146171 -> ciflow/inductor/146171 2025-03-04T20:11:07.3757691Z * [new tag] ciflow/inductor/146172 -> ciflow/inductor/146172 2025-03-04T20:11:07.3757819Z * [new tag] ciflow/inductor/146176 -> ciflow/inductor/146176 2025-03-04T20:11:07.3757981Z * [new tag] ciflow/inductor/146180 -> ciflow/inductor/146180 2025-03-04T20:11:07.3758458Z * [new tag] ciflow/inductor/146218 -> ciflow/inductor/146218 2025-03-04T20:11:07.3758765Z * [new tag] ciflow/inductor/146228 -> ciflow/inductor/146228 2025-03-04T20:11:07.3758912Z * [new tag] ciflow/inductor/146264 -> ciflow/inductor/146264 2025-03-04T20:11:07.3759133Z * [new tag] ciflow/inductor/146267 -> ciflow/inductor/146267 2025-03-04T20:11:07.3759462Z * [new tag] ciflow/inductor/146275 -> ciflow/inductor/146275 2025-03-04T20:11:07.3759849Z * [new tag] ciflow/inductor/146280 -> ciflow/inductor/146280 2025-03-04T20:11:07.3760225Z * [new tag] ciflow/inductor/146288 -> ciflow/inductor/146288 2025-03-04T20:11:07.3760637Z * [new tag] ciflow/inductor/146319 -> ciflow/inductor/146319 2025-03-04T20:11:07.3762358Z * [new tag] ciflow/inductor/146335 -> ciflow/inductor/146335 2025-03-04T20:11:07.3762760Z * [new tag] ciflow/inductor/146341 -> ciflow/inductor/146341 2025-03-04T20:11:07.3762899Z * [new tag] ciflow/inductor/146393 -> ciflow/inductor/146393 2025-03-04T20:11:07.3763042Z * [new tag] ciflow/inductor/146395 -> ciflow/inductor/146395 2025-03-04T20:11:07.3763175Z * [new tag] ciflow/inductor/146415 -> ciflow/inductor/146415 2025-03-04T20:11:07.3763304Z * [new tag] ciflow/inductor/146421 -> ciflow/inductor/146421 2025-03-04T20:11:07.3763564Z * [new tag] ciflow/inductor/146436 -> ciflow/inductor/146436 2025-03-04T20:11:07.3763958Z * [new tag] ciflow/inductor/146455 -> ciflow/inductor/146455 2025-03-04T20:11:07.3764340Z * [new tag] ciflow/inductor/146499 -> ciflow/inductor/146499 2025-03-04T20:11:07.3764650Z * [new tag] ciflow/inductor/146500 -> ciflow/inductor/146500 2025-03-04T20:11:07.3765089Z * [new tag] ciflow/inductor/146501 -> ciflow/inductor/146501 2025-03-04T20:11:07.3765523Z * [new tag] ciflow/inductor/146502 -> ciflow/inductor/146502 2025-03-04T20:11:07.3765957Z * [new tag] ciflow/inductor/146504 -> ciflow/inductor/146504 2025-03-04T20:11:07.3766257Z * [new tag] ciflow/inductor/146505 -> ciflow/inductor/146505 2025-03-04T20:11:07.3766690Z * [new tag] ciflow/inductor/146506 -> ciflow/inductor/146506 2025-03-04T20:11:07.3767097Z * [new tag] ciflow/inductor/146526 -> ciflow/inductor/146526 2025-03-04T20:11:07.3768968Z * [new tag] ciflow/inductor/146530 -> ciflow/inductor/146530 2025-03-04T20:11:07.3769135Z * [new tag] ciflow/inductor/146535 -> ciflow/inductor/146535 2025-03-04T20:11:07.3769291Z * [new tag] ciflow/inductor/146558 -> ciflow/inductor/146558 2025-03-04T20:11:07.3769432Z * [new tag] ciflow/inductor/146561 -> ciflow/inductor/146561 2025-03-04T20:11:07.3769742Z * [new tag] ciflow/inductor/146562 -> ciflow/inductor/146562 2025-03-04T20:11:07.3770047Z * [new tag] ciflow/inductor/146636 -> ciflow/inductor/146636 2025-03-04T20:11:07.3770570Z * [new tag] ciflow/inductor/146661 -> ciflow/inductor/146661 2025-03-04T20:11:07.3770979Z * [new tag] ciflow/inductor/146678 -> ciflow/inductor/146678 2025-03-04T20:11:07.3771260Z * [new tag] ciflow/inductor/146706 -> ciflow/inductor/146706 2025-03-04T20:11:07.3771745Z * [new tag] ciflow/inductor/146718 -> ciflow/inductor/146718 2025-03-04T20:11:07.3772127Z * [new tag] ciflow/inductor/146779 -> ciflow/inductor/146779 2025-03-04T20:11:07.3772801Z * [new tag] ciflow/inductor/146781 -> ciflow/inductor/146781 2025-03-04T20:11:07.3773168Z * [new tag] ciflow/inductor/146823 -> ciflow/inductor/146823 2025-03-04T20:11:07.3773612Z * [new tag] ciflow/inductor/146826 -> ciflow/inductor/146826 2025-03-04T20:11:07.3774179Z * [new tag] ciflow/inductor/146827 -> ciflow/inductor/146827 2025-03-04T20:11:07.3774711Z * [new tag] ciflow/inductor/146844 -> ciflow/inductor/146844 2025-03-04T20:11:07.3775139Z * [new tag] ciflow/inductor/146845 -> ciflow/inductor/146845 2025-03-04T20:11:07.3775581Z * [new tag] ciflow/inductor/146850 -> ciflow/inductor/146850 2025-03-04T20:11:07.3776035Z * [new tag] ciflow/inductor/146864 -> ciflow/inductor/146864 2025-03-04T20:11:07.3776360Z * [new tag] ciflow/inductor/146870 -> ciflow/inductor/146870 2025-03-04T20:11:07.3776798Z * [new tag] ciflow/inductor/146873 -> ciflow/inductor/146873 2025-03-04T20:11:07.3779399Z * [new tag] ciflow/inductor/146874 -> ciflow/inductor/146874 2025-03-04T20:11:07.3779587Z * [new tag] ciflow/inductor/146894 -> ciflow/inductor/146894 2025-03-04T20:11:07.3779758Z * [new tag] ciflow/inductor/146895 -> ciflow/inductor/146895 2025-03-04T20:11:07.3779901Z * [new tag] ciflow/inductor/146919 -> ciflow/inductor/146919 2025-03-04T20:11:07.3780036Z * [new tag] ciflow/inductor/146921 -> ciflow/inductor/146921 2025-03-04T20:11:07.3780740Z * [new tag] ciflow/inductor/146928 -> ciflow/inductor/146928 2025-03-04T20:11:07.3780934Z * [new tag] ciflow/inductor/146935 -> ciflow/inductor/146935 2025-03-04T20:11:07.3781225Z * [new tag] ciflow/inductor/146942 -> ciflow/inductor/146942 2025-03-04T20:11:07.3781522Z * [new tag] ciflow/inductor/146962 -> ciflow/inductor/146962 2025-03-04T20:11:07.3781835Z * [new tag] ciflow/inductor/146983 -> ciflow/inductor/146983 2025-03-04T20:11:07.3782493Z * [new tag] ciflow/inductor/146989 -> ciflow/inductor/146989 2025-03-04T20:11:07.3785509Z * [new tag] ciflow/inductor/147007 -> ciflow/inductor/147007 2025-03-04T20:11:07.3785850Z * [new tag] ciflow/inductor/147014 -> ciflow/inductor/147014 2025-03-04T20:11:07.3786062Z * [new tag] ciflow/inductor/147021 -> ciflow/inductor/147021 2025-03-04T20:11:07.3786245Z * [new tag] ciflow/inductor/147036 -> ciflow/inductor/147036 2025-03-04T20:11:07.3786514Z * [new tag] ciflow/inductor/147049 -> ciflow/inductor/147049 2025-03-04T20:11:07.3786762Z * [new tag] ciflow/inductor/147105 -> ciflow/inductor/147105 2025-03-04T20:11:07.3787281Z * [new tag] ciflow/inductor/147146 -> ciflow/inductor/147146 2025-03-04T20:11:07.3787475Z * [new tag] ciflow/inductor/147149 -> ciflow/inductor/147149 2025-03-04T20:11:07.3787613Z * [new tag] ciflow/inductor/147155 -> ciflow/inductor/147155 2025-03-04T20:11:07.3787762Z * [new tag] ciflow/inductor/147178 -> ciflow/inductor/147178 2025-03-04T20:11:07.3787892Z * [new tag] ciflow/inductor/147205 -> ciflow/inductor/147205 2025-03-04T20:11:07.3788031Z * [new tag] ciflow/inductor/147225 -> ciflow/inductor/147225 2025-03-04T20:11:07.3788335Z * [new tag] ciflow/inductor/147229 -> ciflow/inductor/147229 2025-03-04T20:11:07.3788689Z * [new tag] ciflow/inductor/147269 -> ciflow/inductor/147269 2025-03-04T20:11:07.3790829Z * [new tag] ciflow/inductor/147272 -> ciflow/inductor/147272 2025-03-04T20:11:07.3791193Z * [new tag] ciflow/inductor/147314 -> ciflow/inductor/147314 2025-03-04T20:11:07.3791446Z * [new tag] ciflow/inductor/147315 -> ciflow/inductor/147315 2025-03-04T20:11:07.3791851Z * [new tag] ciflow/inductor/147320 -> ciflow/inductor/147320 2025-03-04T20:11:07.3792291Z * [new tag] ciflow/inductor/147341 -> ciflow/inductor/147341 2025-03-04T20:11:07.3792951Z * [new tag] ciflow/inductor/147360 -> ciflow/inductor/147360 2025-03-04T20:11:07.3793296Z * [new tag] ciflow/inductor/147368 -> ciflow/inductor/147368 2025-03-04T20:11:07.3793820Z * [new tag] ciflow/inductor/147403 -> ciflow/inductor/147403 2025-03-04T20:11:07.3794186Z * [new tag] ciflow/inductor/147410 -> ciflow/inductor/147410 2025-03-04T20:11:07.3794673Z * [new tag] ciflow/inductor/147414 -> ciflow/inductor/147414 2025-03-04T20:11:07.3795087Z * [new tag] ciflow/inductor/147415 -> ciflow/inductor/147415 2025-03-04T20:11:07.3795594Z * [new tag] ciflow/inductor/147422 -> ciflow/inductor/147422 2025-03-04T20:11:07.3799502Z * [new tag] ciflow/inductor/147445 -> ciflow/inductor/147445 2025-03-04T20:11:07.3799692Z * [new tag] ciflow/inductor/147452 -> ciflow/inductor/147452 2025-03-04T20:11:07.3799852Z * [new tag] ciflow/inductor/147481 -> ciflow/inductor/147481 2025-03-04T20:11:07.3799993Z * [new tag] ciflow/inductor/147485 -> ciflow/inductor/147485 2025-03-04T20:11:07.3800126Z * [new tag] ciflow/inductor/147498 -> ciflow/inductor/147498 2025-03-04T20:11:07.3800264Z * [new tag] ciflow/inductor/147514 -> ciflow/inductor/147514 2025-03-04T20:11:07.3800395Z * [new tag] ciflow/inductor/147528 -> ciflow/inductor/147528 2025-03-04T20:11:07.3800536Z * [new tag] ciflow/inductor/147552 -> ciflow/inductor/147552 2025-03-04T20:11:07.3800889Z * [new tag] ciflow/inductor/147557 -> ciflow/inductor/147557 2025-03-04T20:11:07.3801027Z * [new tag] ciflow/inductor/147561 -> ciflow/inductor/147561 2025-03-04T20:11:07.3801192Z * [new tag] ciflow/inductor/147562 -> ciflow/inductor/147562 2025-03-04T20:11:07.3801384Z * [new tag] ciflow/inductor/147574 -> ciflow/inductor/147574 2025-03-04T20:11:07.3801606Z * [new tag] ciflow/inductor/147583 -> ciflow/inductor/147583 2025-03-04T20:11:07.3802190Z * [new tag] ciflow/inductor/147592 -> ciflow/inductor/147592 2025-03-04T20:11:07.3802438Z * [new tag] ciflow/inductor/147603 -> ciflow/inductor/147603 2025-03-04T20:11:07.3802999Z * [new tag] ciflow/inductor/147619 -> ciflow/inductor/147619 2025-03-04T20:11:07.3803305Z * [new tag] ciflow/inductor/147648 -> ciflow/inductor/147648 2025-03-04T20:11:07.3803806Z * [new tag] ciflow/inductor/147660 -> ciflow/inductor/147660 2025-03-04T20:11:07.3804116Z * [new tag] ciflow/inductor/147727 -> ciflow/inductor/147727 2025-03-04T20:11:07.3804630Z * [new tag] ciflow/inductor/147741 -> ciflow/inductor/147741 2025-03-04T20:11:07.3804997Z * [new tag] ciflow/inductor/147745 -> ciflow/inductor/147745 2025-03-04T20:11:07.3805797Z * [new tag] ciflow/inductor/147768 -> ciflow/inductor/147768 2025-03-04T20:11:07.3806193Z * [new tag] ciflow/inductor/147790 -> ciflow/inductor/147790 2025-03-04T20:11:07.3806341Z * [new tag] ciflow/inductor/147797 -> ciflow/inductor/147797 2025-03-04T20:11:07.3806718Z * [new tag] ciflow/inductor/147798 -> ciflow/inductor/147798 2025-03-04T20:11:07.3807181Z * [new tag] ciflow/inductor/147800 -> ciflow/inductor/147800 2025-03-04T20:11:07.3807560Z * [new tag] ciflow/inductor/147817 -> ciflow/inductor/147817 2025-03-04T20:11:07.3808081Z * [new tag] ciflow/inductor/147821 -> ciflow/inductor/147821 2025-03-04T20:11:07.3808457Z * [new tag] ciflow/inductor/147836 -> ciflow/inductor/147836 2025-03-04T20:11:07.3809053Z * [new tag] ciflow/inductor/147863 -> ciflow/inductor/147863 2025-03-04T20:11:07.3809362Z * [new tag] ciflow/inductor/147870 -> ciflow/inductor/147870 2025-03-04T20:11:07.3809853Z * [new tag] ciflow/inductor/147881 -> ciflow/inductor/147881 2025-03-04T20:11:07.3810255Z * [new tag] ciflow/inductor/147899 -> ciflow/inductor/147899 2025-03-04T20:11:07.3810718Z * [new tag] ciflow/inductor/147902 -> ciflow/inductor/147902 2025-03-04T20:11:07.3811077Z * [new tag] ciflow/inductor/147903 -> ciflow/inductor/147903 2025-03-04T20:11:07.3812016Z * [new tag] ciflow/inductor/147908 -> ciflow/inductor/147908 2025-03-04T20:11:07.3812245Z * [new tag] ciflow/inductor/147910 -> ciflow/inductor/147910 2025-03-04T20:11:07.3812581Z * [new tag] ciflow/inductor/147915 -> ciflow/inductor/147915 2025-03-04T20:11:07.3812976Z * [new tag] ciflow/inductor/147917 -> ciflow/inductor/147917 2025-03-04T20:11:07.3813493Z * [new tag] ciflow/inductor/147927 -> ciflow/inductor/147927 2025-03-04T20:11:07.3813876Z * [new tag] ciflow/inductor/147945 -> ciflow/inductor/147945 2025-03-04T20:11:07.3814487Z * [new tag] ciflow/inductor/147955 -> ciflow/inductor/147955 2025-03-04T20:11:07.3814891Z * [new tag] ciflow/inductor/147956 -> ciflow/inductor/147956 2025-03-04T20:11:07.3815775Z * [new tag] ciflow/inductor/147957 -> ciflow/inductor/147957 2025-03-04T20:11:07.3815922Z * [new tag] ciflow/inductor/147958 -> ciflow/inductor/147958 2025-03-04T20:11:07.3816518Z * [new tag] ciflow/inductor/147959 -> ciflow/inductor/147959 2025-03-04T20:11:07.3816919Z * [new tag] ciflow/inductor/147960 -> ciflow/inductor/147960 2025-03-04T20:11:07.3817405Z * [new tag] ciflow/inductor/147962 -> ciflow/inductor/147962 2025-03-04T20:11:07.3817836Z * [new tag] ciflow/inductor/147990 -> ciflow/inductor/147990 2025-03-04T20:11:07.3818355Z * [new tag] ciflow/inductor/148002 -> ciflow/inductor/148002 2025-03-04T20:11:07.3818814Z * [new tag] ciflow/inductor/148007 -> ciflow/inductor/148007 2025-03-04T20:11:07.3819353Z * [new tag] ciflow/inductor/148008 -> ciflow/inductor/148008 2025-03-04T20:11:07.3819759Z * [new tag] ciflow/inductor/148010 -> ciflow/inductor/148010 2025-03-04T20:11:07.3820621Z * [new tag] ciflow/inductor/148042 -> ciflow/inductor/148042 2025-03-04T20:11:07.3820851Z * [new tag] ciflow/inductor/148046 -> ciflow/inductor/148046 2025-03-04T20:11:07.3821417Z * [new tag] ciflow/inductor/148063 -> ciflow/inductor/148063 2025-03-04T20:11:07.3821793Z * [new tag] ciflow/inductor/148083 -> ciflow/inductor/148083 2025-03-04T20:11:07.3822505Z * [new tag] ciflow/inductor/148091 -> ciflow/inductor/148091 2025-03-04T20:11:07.3822816Z * [new tag] ciflow/inductor/148092 -> ciflow/inductor/148092 2025-03-04T20:11:07.3823307Z * [new tag] ciflow/inductor/148104 -> ciflow/inductor/148104 2025-03-04T20:11:07.3823779Z * [new tag] ciflow/inductor/148130 -> ciflow/inductor/148130 2025-03-04T20:11:07.3824426Z * [new tag] ciflow/inductor/148131 -> ciflow/inductor/148131 2025-03-04T20:11:07.3824712Z * [new tag] ciflow/inductor/148132 -> ciflow/inductor/148132 2025-03-04T20:11:07.3825257Z * [new tag] ciflow/inductor/148138 -> ciflow/inductor/148138 2025-03-04T20:11:07.3825633Z * [new tag] ciflow/inductor/148139 -> ciflow/inductor/148139 2025-03-04T20:11:07.3826352Z * [new tag] ciflow/inductor/148160 -> ciflow/inductor/148160 2025-03-04T20:11:07.3826717Z * [new tag] ciflow/inductor/148163 -> ciflow/inductor/148163 2025-03-04T20:11:07.3827157Z * [new tag] ciflow/inductor/148173 -> ciflow/inductor/148173 2025-03-04T20:11:07.3827653Z * [new tag] ciflow/inductor/148174 -> ciflow/inductor/148174 2025-03-04T20:11:07.3828211Z * [new tag] ciflow/inductor/148176 -> ciflow/inductor/148176 2025-03-04T20:11:07.3829276Z * [new tag] ciflow/inductor/148186 -> ciflow/inductor/148186 2025-03-04T20:11:07.3829547Z * [new tag] ciflow/inductor/148190 -> ciflow/inductor/148190 2025-03-04T20:11:07.3830016Z * [new tag] ciflow/inductor/148202 -> ciflow/inductor/148202 2025-03-04T20:11:07.3830378Z * [new tag] ciflow/inductor/148205 -> ciflow/inductor/148205 2025-03-04T20:11:07.3831162Z * [new tag] ciflow/inductor/148206 -> ciflow/inductor/148206 2025-03-04T20:11:07.3831411Z * [new tag] ciflow/inductor/148209 -> ciflow/inductor/148209 2025-03-04T20:11:07.3831896Z * [new tag] ciflow/inductor/148210 -> ciflow/inductor/148210 2025-03-04T20:11:07.3832274Z * [new tag] ciflow/inductor/148212 -> ciflow/inductor/148212 2025-03-04T20:11:07.3832989Z * [new tag] ciflow/inductor/148220 -> ciflow/inductor/148220 2025-03-04T20:11:07.3833349Z * [new tag] ciflow/inductor/148223 -> ciflow/inductor/148223 2025-03-04T20:11:07.3834303Z * [new tag] ciflow/inductor/148231 -> ciflow/inductor/148231 2025-03-04T20:11:07.3834514Z * [new tag] ciflow/inductor/148233 -> ciflow/inductor/148233 2025-03-04T20:11:07.3834953Z * [new tag] ciflow/inductor/148234 -> ciflow/inductor/148234 2025-03-04T20:11:07.3835318Z * [new tag] ciflow/inductor/148235 -> ciflow/inductor/148235 2025-03-04T20:11:07.3836028Z * [new tag] ciflow/inductor/148236 -> ciflow/inductor/148236 2025-03-04T20:11:07.3836327Z * [new tag] ciflow/inductor/148243 -> ciflow/inductor/148243 2025-03-04T20:11:07.3837158Z * [new tag] ciflow/inductor/148260 -> ciflow/inductor/148260 2025-03-04T20:11:07.3837412Z * [new tag] ciflow/inductor/148261 -> ciflow/inductor/148261 2025-03-04T20:11:07.3837835Z * [new tag] ciflow/inductor/148279 -> ciflow/inductor/148279 2025-03-04T20:11:07.3838189Z * [new tag] ciflow/inductor/148288 -> ciflow/inductor/148288 2025-03-04T20:11:07.3839088Z * [new tag] ciflow/inductor/148290 -> ciflow/inductor/148290 2025-03-04T20:11:07.3839307Z * [new tag] ciflow/inductor/148292 -> ciflow/inductor/148292 2025-03-04T20:11:07.3839863Z * [new tag] ciflow/inductor/148294 -> ciflow/inductor/148294 2025-03-04T20:11:07.3840251Z * [new tag] ciflow/inductor/148303 -> ciflow/inductor/148303 2025-03-04T20:11:07.3841142Z * [new tag] ciflow/inductor/148305 -> ciflow/inductor/148305 2025-03-04T20:11:07.3841290Z * [new tag] ciflow/inductor/148323 -> ciflow/inductor/148323 2025-03-04T20:11:07.3841806Z * [new tag] ciflow/inductor/148328 -> ciflow/inductor/148328 2025-03-04T20:11:07.3842162Z * [new tag] ciflow/inductor/148357 -> ciflow/inductor/148357 2025-03-04T20:11:07.3842806Z * [new tag] ciflow/inductor/148358 -> ciflow/inductor/148358 2025-03-04T20:11:07.3843179Z * [new tag] ciflow/inductor/148359 -> ciflow/inductor/148359 2025-03-04T20:11:07.3844085Z * [new tag] ciflow/inductor/148363 -> ciflow/inductor/148363 2025-03-04T20:11:07.3844364Z * [new tag] ciflow/inductor/148364 -> ciflow/inductor/148364 2025-03-04T20:11:07.3844854Z * [new tag] ciflow/inductor/148366 -> ciflow/inductor/148366 2025-03-04T20:11:07.3845175Z * [new tag] ciflow/inductor/148367 -> ciflow/inductor/148367 2025-03-04T20:11:07.3845829Z * [new tag] ciflow/inductor/148376 -> ciflow/inductor/148376 2025-03-04T20:11:07.3846167Z * [new tag] ciflow/inductor/148377 -> ciflow/inductor/148377 2025-03-04T20:11:07.3847091Z * [new tag] ciflow/inductor/148380 -> ciflow/inductor/148380 2025-03-04T20:11:07.3847355Z * [new tag] ciflow/inductor/148381 -> ciflow/inductor/148381 2025-03-04T20:11:07.3847802Z * [new tag] ciflow/inductor/148385 -> ciflow/inductor/148385 2025-03-04T20:11:07.3848233Z * [new tag] ciflow/inductor/148386 -> ciflow/inductor/148386 2025-03-04T20:11:07.3848739Z * [new tag] ciflow/inductor/148401 -> ciflow/inductor/148401 2025-03-04T20:11:07.3849245Z * [new tag] ciflow/inductor/148407 -> ciflow/inductor/148407 2025-03-04T20:11:07.3849646Z * [new tag] ciflow/inductor/148413 -> ciflow/inductor/148413 2025-03-04T20:11:07.3850532Z * [new tag] ciflow/inductor/148414 -> ciflow/inductor/148414 2025-03-04T20:11:07.3850713Z * [new tag] ciflow/inductor/148415 -> ciflow/inductor/148415 2025-03-04T20:11:07.3851441Z * [new tag] ciflow/inductor/148418 -> ciflow/inductor/148418 2025-03-04T20:11:07.3851740Z * [new tag] ciflow/inductor/148423 -> ciflow/inductor/148423 2025-03-04T20:11:07.3852686Z * [new tag] ciflow/inductor/148424 -> ciflow/inductor/148424 2025-03-04T20:11:07.3852880Z * [new tag] ciflow/inductor/148430 -> ciflow/inductor/148430 2025-03-04T20:11:07.3854133Z * [new tag] ciflow/inductor/148432 -> ciflow/inductor/148432 2025-03-04T20:11:07.3854343Z * [new tag] ciflow/inductor/148445 -> ciflow/inductor/148445 2025-03-04T20:11:07.3855242Z * [new tag] ciflow/inductor/148450 -> ciflow/inductor/148450 2025-03-04T20:11:07.3855519Z * [new tag] ciflow/inductor/148454 -> ciflow/inductor/148454 2025-03-04T20:11:07.3855973Z * [new tag] ciflow/inductor/148459 -> ciflow/inductor/148459 2025-03-04T20:11:07.3856352Z * [new tag] ciflow/inductor/148470 -> ciflow/inductor/148470 2025-03-04T20:11:07.3857109Z * [new tag] ciflow/inductor/148473 -> ciflow/inductor/148473 2025-03-04T20:11:07.3857473Z * [new tag] ciflow/inductor/3b9a386 -> ciflow/inductor/3b9a386 2025-03-04T20:11:07.3858501Z * [new tag] ciflow/inductor/3d4b92b -> ciflow/inductor/3d4b92b 2025-03-04T20:11:07.3858728Z * [new tag] ciflow/inductor/88106 -> ciflow/inductor/88106 2025-03-04T20:11:07.3859707Z * [new tag] ciflow/inductor/88196 -> ciflow/inductor/88196 2025-03-04T20:11:07.3859991Z * [new tag] ciflow/inductor/88998 -> ciflow/inductor/88998 2025-03-04T20:11:07.3860940Z * [new tag] ciflow/inductor/d224ac7 -> ciflow/inductor/d224ac7 2025-03-04T20:11:07.3861205Z * [new tag] ciflow/linux-aarch64/125888 -> ciflow/linux-aarch64/125888 2025-03-04T20:11:07.3861714Z * [new tag] ciflow/linux-aarch64/126050 -> ciflow/linux-aarch64/126050 2025-03-04T20:11:07.3862050Z * [new tag] ciflow/linux-aarch64/126054 -> ciflow/linux-aarch64/126054 2025-03-04T20:11:07.3862458Z * [new tag] ciflow/linux-aarch64/133297 -> ciflow/linux-aarch64/133297 2025-03-04T20:11:07.3862729Z * [new tag] ciflow/linux-aarch64/133315 -> ciflow/linux-aarch64/133315 2025-03-04T20:11:07.3863267Z * [new tag] ciflow/linux-aarch64/133392 -> ciflow/linux-aarch64/133392 2025-03-04T20:11:07.3863500Z * [new tag] ciflow/linux-aarch64/133419 -> ciflow/linux-aarch64/133419 2025-03-04T20:11:07.3864092Z * [new tag] ciflow/linux-aarch64/133423 -> ciflow/linux-aarch64/133423 2025-03-04T20:11:07.3864290Z * [new tag] ciflow/linux-aarch64/133667 -> ciflow/linux-aarch64/133667 2025-03-04T20:11:07.3864822Z * [new tag] ciflow/linux-aarch64/133753 -> ciflow/linux-aarch64/133753 2025-03-04T20:11:07.3865203Z * [new tag] ciflow/linux-aarch64/135058 -> ciflow/linux-aarch64/135058 2025-03-04T20:11:07.3866265Z * [new tag] ciflow/linux-aarch64/135333 -> ciflow/linux-aarch64/135333 2025-03-04T20:11:07.3866537Z * [new tag] ciflow/linux-aarch64/135792 -> ciflow/linux-aarch64/135792 2025-03-04T20:11:07.3866955Z * [new tag] ciflow/linux-aarch64/136355 -> ciflow/linux-aarch64/136355 2025-03-04T20:11:07.3867362Z * [new tag] ciflow/linux-aarch64/137568 -> ciflow/linux-aarch64/137568 2025-03-04T20:11:07.3868468Z * [new tag] ciflow/linux-aarch64/138388 -> ciflow/linux-aarch64/138388 2025-03-04T20:11:07.3868712Z * [new tag] ciflow/linux-aarch64/138889 -> ciflow/linux-aarch64/138889 2025-03-04T20:11:07.3868900Z * [new tag] ciflow/linux-aarch64/140159 -> ciflow/linux-aarch64/140159 2025-03-04T20:11:07.3869554Z * [new tag] ciflow/linux-aarch64/143741 -> ciflow/linux-aarch64/143741 2025-03-04T20:11:07.3869885Z * [new tag] ciflow/linux-aarch64/145942 -> ciflow/linux-aarch64/145942 2025-03-04T20:11:07.3870113Z * [new tag] ciflow/linux-aarch64/146823 -> ciflow/linux-aarch64/146823 2025-03-04T20:11:07.3870543Z * [new tag] ciflow/linux-aarch64/146826 -> ciflow/linux-aarch64/146826 2025-03-04T20:11:07.3870927Z * [new tag] ciflow/linux-aarch64/146895 -> ciflow/linux-aarch64/146895 2025-03-04T20:11:07.3871410Z * [new tag] ciflow/linux-aarch64/147073 -> ciflow/linux-aarch64/147073 2025-03-04T20:11:07.3871892Z * [new tag] ciflow/linux-aarch64/147337 -> ciflow/linux-aarch64/147337 2025-03-04T20:11:07.3872395Z * [new tag] ciflow/linux-aarch64/147341 -> ciflow/linux-aarch64/147341 2025-03-04T20:11:07.3873135Z * [new tag] ciflow/linux-aarch64/147359 -> ciflow/linux-aarch64/147359 2025-03-04T20:11:07.3873541Z * [new tag] ciflow/linux-aarch64/147498 -> ciflow/linux-aarch64/147498 2025-03-04T20:11:07.3873698Z * [new tag] ciflow/linux-aarch64/147763 -> ciflow/linux-aarch64/147763 2025-03-04T20:11:07.3874085Z * [new tag] ciflow/linux-aarch64/147817 -> ciflow/linux-aarch64/147817 2025-03-04T20:11:07.3874299Z * [new tag] ciflow/linux-aarch64/147855 -> ciflow/linux-aarch64/147855 2025-03-04T20:11:07.3874664Z * [new tag] ciflow/linux-aarch64/147917 -> ciflow/linux-aarch64/147917 2025-03-04T20:11:07.3875125Z * [new tag] ciflow/linux-aarch64/147945 -> ciflow/linux-aarch64/147945 2025-03-04T20:11:07.3875486Z * [new tag] ciflow/linux-aarch64/147955 -> ciflow/linux-aarch64/147955 2025-03-04T20:11:07.3875868Z * [new tag] ciflow/linux-aarch64/147956 -> ciflow/linux-aarch64/147956 2025-03-04T20:11:07.3876209Z * [new tag] ciflow/linux-aarch64/147957 -> ciflow/linux-aarch64/147957 2025-03-04T20:11:07.3881329Z * [new tag] ciflow/linux-aarch64/147958 -> ciflow/linux-aarch64/147958 2025-03-04T20:11:07.3881545Z * [new tag] ciflow/linux-aarch64/147959 -> ciflow/linux-aarch64/147959 2025-03-04T20:11:07.3881719Z * [new tag] ciflow/linux-aarch64/147964 -> ciflow/linux-aarch64/147964 2025-03-04T20:11:07.3881872Z * [new tag] ciflow/linux-aarch64/148076 -> ciflow/linux-aarch64/148076 2025-03-04T20:11:07.3882017Z * [new tag] ciflow/linux-aarch64/148163 -> ciflow/linux-aarch64/148163 2025-03-04T20:11:07.3882152Z * [new tag] ciflow/linux-aarch64/148173 -> ciflow/linux-aarch64/148173 2025-03-04T20:11:07.3882444Z * [new tag] ciflow/linux-aarch64/148403 -> ciflow/linux-aarch64/148403 2025-03-04T20:11:07.3882580Z * [new tag] ciflow/mps/102148 -> ciflow/mps/102148 2025-03-04T20:11:07.3882707Z * [new tag] ciflow/mps/119496 -> ciflow/mps/119496 2025-03-04T20:11:07.3882822Z * [new tag] ciflow/mps/120076 -> ciflow/mps/120076 2025-03-04T20:11:07.3882953Z * [new tag] ciflow/mps/133423 -> ciflow/mps/133423 2025-03-04T20:11:07.3883071Z * [new tag] ciflow/mps/133667 -> ciflow/mps/133667 2025-03-04T20:11:07.3883198Z * [new tag] ciflow/mps/138640 -> ciflow/mps/138640 2025-03-04T20:11:07.3883311Z * [new tag] ciflow/mps/139469 -> ciflow/mps/139469 2025-03-04T20:11:07.3883456Z * [new tag] ciflow/mps/140159 -> ciflow/mps/140159 2025-03-04T20:11:07.3883878Z * [new tag] ciflow/mps/140211 -> ciflow/mps/140211 2025-03-04T20:11:07.3885490Z * [new tag] ciflow/mps/140725 -> ciflow/mps/140725 2025-03-04T20:11:07.3885774Z * [new tag] ciflow/mps/142097 -> ciflow/mps/142097 2025-03-04T20:11:07.3885912Z * [new tag] ciflow/mps/142202 -> ciflow/mps/142202 2025-03-04T20:11:07.3886342Z * [new tag] ciflow/mps/142477 -> ciflow/mps/142477 2025-03-04T20:11:07.3886801Z * [new tag] ciflow/mps/143630 -> ciflow/mps/143630 2025-03-04T20:11:07.3887272Z * [new tag] ciflow/mps/143666 -> ciflow/mps/143666 2025-03-04T20:11:07.3887723Z * [new tag] ciflow/mps/143911 -> ciflow/mps/143911 2025-03-04T20:11:07.3887951Z * [new tag] ciflow/mps/143966 -> ciflow/mps/143966 2025-03-04T20:11:07.3888474Z * [new tag] ciflow/mps/144405 -> ciflow/mps/144405 2025-03-04T20:11:07.3889829Z * [new tag] ciflow/mps/144664 -> ciflow/mps/144664 2025-03-04T20:11:07.3890015Z * [new tag] ciflow/mps/145955 -> ciflow/mps/145955 2025-03-04T20:11:07.3890535Z * [new tag] ciflow/mps/146098 -> ciflow/mps/146098 2025-03-04T20:11:07.3890836Z * [new tag] ciflow/mps/146436 -> ciflow/mps/146436 2025-03-04T20:11:07.3891486Z * [new tag] ciflow/mps/146754 -> ciflow/mps/146754 2025-03-04T20:11:07.3891738Z * [new tag] ciflow/mps/146989 -> ciflow/mps/146989 2025-03-04T20:11:07.3892162Z * [new tag] ciflow/mps/147205 -> ciflow/mps/147205 2025-03-04T20:11:07.3892545Z * [new tag] ciflow/mps/147583 -> ciflow/mps/147583 2025-03-04T20:11:07.3895560Z * [new tag] ciflow/mps/147644 -> ciflow/mps/147644 2025-03-04T20:11:07.3895800Z * [new tag] ciflow/mps/147893 -> ciflow/mps/147893 2025-03-04T20:11:07.3899970Z * [new tag] ciflow/mps/148305 -> ciflow/mps/148305 2025-03-04T20:11:07.3900144Z * [new tag] ciflow/mps/148350 -> ciflow/mps/148350 2025-03-04T20:11:07.3900271Z * [new tag] ciflow/mps/148415 -> ciflow/mps/148415 2025-03-04T20:11:07.3900413Z * [new tag] ciflow/mps/148449 -> ciflow/mps/148449 2025-03-04T20:11:07.3900541Z * [new tag] ciflow/mps/148468 -> ciflow/mps/148468 2025-03-04T20:11:07.3900662Z * [new tag] ciflow/mps/148471 -> ciflow/mps/148471 2025-03-04T20:11:07.3900844Z * [new tag] ciflow/op-benchmark/143733 -> ciflow/op-benchmark/143733 2025-03-04T20:11:07.3900997Z * [new tag] ciflow/periodic/054a2fd -> ciflow/periodic/054a2fd 2025-03-04T20:11:07.3901150Z * [new tag] ciflow/periodic/123020 -> ciflow/periodic/123020 2025-03-04T20:11:07.3901480Z * [new tag] ciflow/periodic/134817 -> ciflow/periodic/134817 2025-03-04T20:11:07.3901633Z * [new tag] ciflow/periodic/140989 -> ciflow/periodic/140989 2025-03-04T20:11:07.3901771Z * [new tag] ciflow/periodic/141309 -> ciflow/periodic/141309 2025-03-04T20:11:07.3901916Z * [new tag] ciflow/periodic/141355 -> ciflow/periodic/141355 2025-03-04T20:11:07.3902054Z * [new tag] ciflow/periodic/141730 -> ciflow/periodic/141730 2025-03-04T20:11:07.3902208Z * [new tag] ciflow/periodic/142179 -> ciflow/periodic/142179 2025-03-04T20:11:07.3902347Z * [new tag] ciflow/periodic/143959 -> ciflow/periodic/143959 2025-03-04T20:11:07.3902492Z * [new tag] ciflow/periodic/144953 -> ciflow/periodic/144953 2025-03-04T20:11:07.3902629Z * [new tag] ciflow/periodic/146264 -> ciflow/periodic/146264 2025-03-04T20:11:07.3902950Z * [new tag] ciflow/periodic/146403 -> ciflow/periodic/146403 2025-03-04T20:11:07.3903181Z * [new tag] ciflow/periodic/146823 -> ciflow/periodic/146823 2025-03-04T20:11:07.3903432Z * [new tag] ciflow/periodic/146903 -> ciflow/periodic/146903 2025-03-04T20:11:07.3905959Z * [new tag] ciflow/periodic/147459 -> ciflow/periodic/147459 2025-03-04T20:11:07.3906325Z * [new tag] ciflow/periodic/147870 -> ciflow/periodic/147870 2025-03-04T20:11:07.3906578Z * [new tag] ciflow/periodic/148351 -> ciflow/periodic/148351 2025-03-04T20:11:07.3907165Z * [new tag] ciflow/periodic/2a6d37d -> ciflow/periodic/2a6d37d 2025-03-04T20:11:07.3907478Z * [new tag] ciflow/periodic/317eeb8 -> ciflow/periodic/317eeb8 2025-03-04T20:11:07.3907744Z * [new tag] ciflow/periodic/3c32 -> ciflow/periodic/3c32 2025-03-04T20:11:07.3907965Z * [new tag] ciflow/periodic/3e98831 -> ciflow/periodic/3e98831 2025-03-04T20:11:07.3908261Z * [new tag] ciflow/periodic/94512-point -> ciflow/periodic/94512-point 2025-03-04T20:11:07.3908558Z * [new tag] ciflow/periodic/csl/test87519 -> ciflow/periodic/csl/test87519 2025-03-04T20:11:07.3908917Z * [new tag] ciflow/periodic/csltest88275 -> ciflow/periodic/csltest88275 2025-03-04T20:11:07.3909400Z * [new tag] ciflow/periodic/csltest88761 -> ciflow/periodic/csltest88761 2025-03-04T20:11:07.3911541Z * [new tag] ciflow/periodic/release_1.12 -> ciflow/periodic/release_1.12 2025-03-04T20:11:07.3916684Z * [new tag] ciflow/periodic/release_1.12.0 -> ciflow/periodic/release_1.12.0 2025-03-04T20:11:07.3916859Z * [new tag] ciflow/periodic/sha-ec5b83 -> ciflow/periodic/sha-ec5b83 2025-03-04T20:11:07.3917272Z * [new tag] ciflow/riscv64/143979 -> ciflow/riscv64/143979 2025-03-04T20:11:07.3917425Z * [new tag] ciflow/rocm/124424 -> ciflow/rocm/124424 2025-03-04T20:11:07.3917543Z * [new tag] ciflow/rocm/134817 -> ciflow/rocm/134817 2025-03-04T20:11:07.3917665Z * [new tag] ciflow/rocm/137136 -> ciflow/rocm/137136 2025-03-04T20:11:07.3917779Z * [new tag] ciflow/rocm/139469 -> ciflow/rocm/139469 2025-03-04T20:11:07.3917898Z * [new tag] ciflow/rocm/139975 -> ciflow/rocm/139975 2025-03-04T20:11:07.3918012Z * [new tag] ciflow/rocm/140989 -> ciflow/rocm/140989 2025-03-04T20:11:07.3918136Z * [new tag] ciflow/rocm/141309 -> ciflow/rocm/141309 2025-03-04T20:11:07.3918249Z * [new tag] ciflow/rocm/141355 -> ciflow/rocm/141355 2025-03-04T20:11:07.3918368Z * [new tag] ciflow/rocm/142097 -> ciflow/rocm/142097 2025-03-04T20:11:07.3918622Z * [new tag] ciflow/rocm/142859 -> ciflow/rocm/142859 2025-03-04T20:11:07.3918745Z * [new tag] ciflow/rocm/143416 -> ciflow/rocm/143416 2025-03-04T20:11:07.3928489Z * [new tag] ciflow/rocm/143971 -> ciflow/rocm/143971 2025-03-04T20:11:07.3928753Z * [new tag] ciflow/rocm/144120 -> ciflow/rocm/144120 2025-03-04T20:11:07.3928878Z * [new tag] ciflow/rocm/144572 -> ciflow/rocm/144572 2025-03-04T20:11:07.3929001Z * [new tag] ciflow/rocm/144664 -> ciflow/rocm/144664 2025-03-04T20:11:07.3929131Z * [new tag] ciflow/rocm/145475 -> ciflow/rocm/145475 2025-03-04T20:11:07.3929251Z * [new tag] ciflow/rocm/145584 -> ciflow/rocm/145584 2025-03-04T20:11:07.3929363Z * [new tag] ciflow/rocm/145685 -> ciflow/rocm/145685 2025-03-04T20:11:07.3929479Z * [new tag] ciflow/rocm/145946 -> ciflow/rocm/145946 2025-03-04T20:11:07.3929596Z * [new tag] ciflow/rocm/146227 -> ciflow/rocm/146227 2025-03-04T20:11:07.3929715Z * [new tag] ciflow/rocm/146264 -> ciflow/rocm/146264 2025-03-04T20:11:07.3929824Z * [new tag] ciflow/rocm/146448 -> ciflow/rocm/146448 2025-03-04T20:11:07.3929941Z * [new tag] ciflow/rocm/146903 -> ciflow/rocm/146903 2025-03-04T20:11:07.3930052Z * [new tag] ciflow/rocm/147034 -> ciflow/rocm/147034 2025-03-04T20:11:07.3930172Z * [new tag] ciflow/rocm/147243 -> ciflow/rocm/147243 2025-03-04T20:11:07.3930405Z * [new tag] ciflow/rocm/147315 -> ciflow/rocm/147315 2025-03-04T20:11:07.3930529Z * [new tag] ciflow/rocm/147320 -> ciflow/rocm/147320 2025-03-04T20:11:07.3930642Z * [new tag] ciflow/rocm/147382 -> ciflow/rocm/147382 2025-03-04T20:11:07.3930766Z * [new tag] ciflow/rocm/147403 -> ciflow/rocm/147403 2025-03-04T20:11:07.3930878Z * [new tag] ciflow/rocm/147452 -> ciflow/rocm/147452 2025-03-04T20:11:07.3930996Z * [new tag] ciflow/rocm/147459 -> ciflow/rocm/147459 2025-03-04T20:11:07.3931109Z * [new tag] ciflow/rocm/147527 -> ciflow/rocm/147527 2025-03-04T20:11:07.3931233Z * [new tag] ciflow/rocm/147619 -> ciflow/rocm/147619 2025-03-04T20:11:07.3931347Z * [new tag] ciflow/rocm/147630 -> ciflow/rocm/147630 2025-03-04T20:11:07.3931475Z * [new tag] ciflow/rocm/147821 -> ciflow/rocm/147821 2025-03-04T20:11:07.3931599Z * [new tag] ciflow/rocm/147904 -> ciflow/rocm/147904 2025-03-04T20:11:07.3931721Z * [new tag] ciflow/rocm/147993 -> ciflow/rocm/147993 2025-03-04T20:11:07.3931843Z * [new tag] ciflow/rocm/148223 -> ciflow/rocm/148223 2025-03-04T20:11:07.3931959Z * [new tag] ciflow/rocm/148228 -> ciflow/rocm/148228 2025-03-04T20:11:07.3932082Z * [new tag] ciflow/rocm/148371 -> ciflow/rocm/148371 2025-03-04T20:11:07.3932198Z * [new tag] ciflow/rocm/148394 -> ciflow/rocm/148394 2025-03-04T20:11:07.3932321Z * [new tag] ciflow/rocm/148432 -> ciflow/rocm/148432 2025-03-04T20:11:07.3932434Z * [new tag] ciflow/rocm/148437 -> ciflow/rocm/148437 2025-03-04T20:11:07.3932557Z * [new tag] ciflow/s390/142346 -> ciflow/s390/142346 2025-03-04T20:11:07.3932708Z * [new tag] ciflow/s390/143959 -> ciflow/s390/143959 2025-03-04T20:11:07.3935328Z * [new tag] ciflow/s390/148452 -> ciflow/s390/148452 2025-03-04T20:11:07.3935587Z * [new tag] ciflow/slow/01c7106 -> ciflow/slow/01c7106 2025-03-04T20:11:07.3935824Z * [new tag] ciflow/slow/0577043 -> ciflow/slow/0577043 2025-03-04T20:11:07.3936226Z * [new tag] ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym -> ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym 2025-03-04T20:11:07.3936509Z * [new tag] ciflow/slow/0e81104 -> ciflow/slow/0e81104 2025-03-04T20:11:07.3936698Z * [new tag] ciflow/slow/139975 -> ciflow/slow/139975 2025-03-04T20:11:07.3936824Z * [new tag] ciflow/slow/146256 -> ciflow/slow/146256 2025-03-04T20:11:07.3937416Z * [new tag] ciflow/slow/146903 -> ciflow/slow/146903 2025-03-04T20:11:07.3937890Z * [new tag] ciflow/slow/1732077 -> ciflow/slow/1732077 2025-03-04T20:11:07.3939347Z * [new tag] ciflow/slow/187eb7c -> ciflow/slow/187eb7c 2025-03-04T20:11:07.3939511Z * [new tag] ciflow/slow/1faef89 -> ciflow/slow/1faef89 2025-03-04T20:11:07.3939700Z * [new tag] ciflow/slow/3920ec1 -> ciflow/slow/3920ec1 2025-03-04T20:11:07.3943105Z * [new tag] ciflow/slow/3b7c6b2 -> ciflow/slow/3b7c6b2 2025-03-04T20:11:07.3943269Z * [new tag] ciflow/slow/59a3759 -> ciflow/slow/59a3759 2025-03-04T20:11:07.3943401Z * [new tag] ciflow/slow/70ef0bb -> ciflow/slow/70ef0bb 2025-03-04T20:11:07.3943520Z * [new tag] ciflow/slow/788ff06 -> ciflow/slow/788ff06 2025-03-04T20:11:07.3943856Z * [new tag] ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym -> ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym 2025-03-04T20:11:07.3944145Z * [new tag] ciflow/slow/9d85864 -> ciflow/slow/9d85864 2025-03-04T20:11:07.3944292Z * [new tag] ciflow/slow/9ffad5b -> ciflow/slow/9ffad5b 2025-03-04T20:11:07.3944425Z * [new tag] ciflow/slow/a206e8b -> ciflow/slow/a206e8b 2025-03-04T20:11:07.3944940Z * [new tag] ciflow/slow/a837609 -> ciflow/slow/a837609 2025-03-04T20:11:07.3945461Z * [new tag] ciflow/slow/af841f3 -> ciflow/slow/af841f3 2025-03-04T20:11:07.3946542Z * [new tag] ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym -> ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym 2025-03-04T20:11:07.3946689Z * [new tag] ciflow/trunk/108303 -> ciflow/trunk/108303 2025-03-04T20:11:07.3946954Z * [new tag] ciflow/trunk/113257 -> ciflow/trunk/113257 2025-03-04T20:11:07.3947441Z * [new tag] ciflow/trunk/113258 -> ciflow/trunk/113258 2025-03-04T20:11:07.3947748Z * [new tag] ciflow/trunk/120076 -> ciflow/trunk/120076 2025-03-04T20:11:07.3948315Z * [new tag] ciflow/trunk/121445 -> ciflow/trunk/121445 2025-03-04T20:11:07.3948681Z * [new tag] ciflow/trunk/123020 -> ciflow/trunk/123020 2025-03-04T20:11:07.3948972Z * [new tag] ciflow/trunk/124424 -> ciflow/trunk/124424 2025-03-04T20:11:07.3949349Z * [new tag] ciflow/trunk/124490 -> ciflow/trunk/124490 2025-03-04T20:11:07.3953757Z * [new tag] ciflow/trunk/125469 -> ciflow/trunk/125469 2025-03-04T20:11:07.3953927Z * [new tag] ciflow/trunk/125806 -> ciflow/trunk/125806 2025-03-04T20:11:07.3954050Z * [new tag] ciflow/trunk/125888 -> ciflow/trunk/125888 2025-03-04T20:11:07.3954176Z * [new tag] ciflow/trunk/125995 -> ciflow/trunk/125995 2025-03-04T20:11:07.3954314Z * [new tag] ciflow/trunk/126050 -> ciflow/trunk/126050 2025-03-04T20:11:07.3954440Z * [new tag] ciflow/trunk/126054 -> ciflow/trunk/126054 2025-03-04T20:11:07.3954558Z * [new tag] ciflow/trunk/126635 -> ciflow/trunk/126635 2025-03-04T20:11:07.3954818Z * [new tag] ciflow/trunk/127171 -> ciflow/trunk/127171 2025-03-04T20:11:07.3954942Z * [new tag] ciflow/trunk/127919 -> ciflow/trunk/127919 2025-03-04T20:11:07.3955071Z * [new tag] ciflow/trunk/129352 -> ciflow/trunk/129352 2025-03-04T20:11:07.3955192Z * [new tag] ciflow/trunk/129420 -> ciflow/trunk/129420 2025-03-04T20:11:07.3955321Z * [new tag] ciflow/trunk/130141 -> ciflow/trunk/130141 2025-03-04T20:11:07.3955598Z * [new tag] ciflow/trunk/130752 -> ciflow/trunk/130752 2025-03-04T20:11:07.3955888Z * [new tag] ciflow/trunk/131354 -> ciflow/trunk/131354 2025-03-04T20:11:07.3956297Z * [new tag] ciflow/trunk/131507 -> ciflow/trunk/131507 2025-03-04T20:11:07.3956572Z * [new tag] ciflow/trunk/132021 -> ciflow/trunk/132021 2025-03-04T20:11:07.3957297Z * [new tag] ciflow/trunk/133044 -> ciflow/trunk/133044 2025-03-04T20:11:07.3957614Z * [new tag] ciflow/trunk/133289 -> ciflow/trunk/133289 2025-03-04T20:11:07.3957913Z * [new tag] ciflow/trunk/133296 -> ciflow/trunk/133296 2025-03-04T20:11:07.3958314Z * [new tag] ciflow/trunk/133297 -> ciflow/trunk/133297 2025-03-04T20:11:07.3958754Z * [new tag] ciflow/trunk/133315 -> ciflow/trunk/133315 2025-03-04T20:11:07.3959015Z * [new tag] ciflow/trunk/133392 -> ciflow/trunk/133392 2025-03-04T20:11:07.3959518Z * [new tag] ciflow/trunk/133419 -> ciflow/trunk/133419 2025-03-04T20:11:07.3959904Z * [new tag] ciflow/trunk/133423 -> ciflow/trunk/133423 2025-03-04T20:11:07.3960291Z * [new tag] ciflow/trunk/133667 -> ciflow/trunk/133667 2025-03-04T20:11:07.3960633Z * [new tag] ciflow/trunk/133753 -> ciflow/trunk/133753 2025-03-04T20:11:07.3961453Z * [new tag] ciflow/trunk/134219 -> ciflow/trunk/134219 2025-03-04T20:11:07.3961883Z * [new tag] ciflow/trunk/134515 -> ciflow/trunk/134515 2025-03-04T20:11:07.3962092Z * [new tag] ciflow/trunk/135058 -> ciflow/trunk/135058 2025-03-04T20:11:07.3964650Z * [new tag] ciflow/trunk/135631 -> ciflow/trunk/135631 2025-03-04T20:11:07.3964804Z * [new tag] ciflow/trunk/136780 -> ciflow/trunk/136780 2025-03-04T20:11:07.3964959Z * [new tag] ciflow/trunk/136824 -> ciflow/trunk/136824 2025-03-04T20:11:07.3965093Z * [new tag] ciflow/trunk/136835 -> ciflow/trunk/136835 2025-03-04T20:11:07.3965216Z * [new tag] ciflow/trunk/136993 -> ciflow/trunk/136993 2025-03-04T20:11:07.3965335Z * [new tag] ciflow/trunk/137400 -> ciflow/trunk/137400 2025-03-04T20:11:07.3965487Z * [new tag] ciflow/trunk/137580 -> ciflow/trunk/137580 2025-03-04T20:11:07.3965719Z * [new tag] ciflow/trunk/138213 -> ciflow/trunk/138213 2025-03-04T20:11:07.3966110Z * [new tag] ciflow/trunk/138436 -> ciflow/trunk/138436 2025-03-04T20:11:07.3966599Z * [new tag] ciflow/trunk/138626 -> ciflow/trunk/138626 2025-03-04T20:11:07.3967216Z * [new tag] ciflow/trunk/138834 -> ciflow/trunk/138834 2025-03-04T20:11:07.3967419Z * [new tag] ciflow/trunk/138889 -> ciflow/trunk/138889 2025-03-04T20:11:07.3967657Z * [new tag] ciflow/trunk/138996 -> ciflow/trunk/138996 2025-03-04T20:11:07.3968569Z * [new tag] ciflow/trunk/139070 -> ciflow/trunk/139070 2025-03-04T20:11:07.3968812Z * [new tag] ciflow/trunk/139094 -> ciflow/trunk/139094 2025-03-04T20:11:07.3969116Z * [new tag] ciflow/trunk/139971 -> ciflow/trunk/139971 2025-03-04T20:11:07.3969314Z * [new tag] ciflow/trunk/139975 -> ciflow/trunk/139975 2025-03-04T20:11:07.3969683Z * [new tag] ciflow/trunk/140084 -> ciflow/trunk/140084 2025-03-04T20:11:07.3970064Z * [new tag] ciflow/trunk/140159 -> ciflow/trunk/140159 2025-03-04T20:11:07.3971146Z * [new tag] ciflow/trunk/140211 -> ciflow/trunk/140211 2025-03-04T20:11:07.3971302Z * [new tag] ciflow/trunk/140298 -> ciflow/trunk/140298 2025-03-04T20:11:07.3971433Z * [new tag] ciflow/trunk/140323 -> ciflow/trunk/140323 2025-03-04T20:11:07.3971823Z * [new tag] ciflow/trunk/140365 -> ciflow/trunk/140365 2025-03-04T20:11:07.3972293Z * [new tag] ciflow/trunk/140399 -> ciflow/trunk/140399 2025-03-04T20:11:07.3972608Z * [new tag] ciflow/trunk/140793 -> ciflow/trunk/140793 2025-03-04T20:11:07.3973117Z * [new tag] ciflow/trunk/140979 -> ciflow/trunk/140979 2025-03-04T20:11:07.3973568Z * [new tag] ciflow/trunk/140989 -> ciflow/trunk/140989 2025-03-04T20:11:07.3973960Z * [new tag] ciflow/trunk/141178 -> ciflow/trunk/141178 2025-03-04T20:11:07.3974599Z * [new tag] ciflow/trunk/141257 -> ciflow/trunk/141257 2025-03-04T20:11:07.3975029Z * [new tag] ciflow/trunk/141309 -> ciflow/trunk/141309 2025-03-04T20:11:07.3975489Z * [new tag] ciflow/trunk/141730 -> ciflow/trunk/141730 2025-03-04T20:11:07.3975952Z * [new tag] ciflow/trunk/141796 -> ciflow/trunk/141796 2025-03-04T20:11:07.3976290Z * [new tag] ciflow/trunk/141842 -> ciflow/trunk/141842 2025-03-04T20:11:07.3976792Z * [new tag] ciflow/trunk/141889 -> ciflow/trunk/141889 2025-03-04T20:11:07.3977135Z * [new tag] ciflow/trunk/141910 -> ciflow/trunk/141910 2025-03-04T20:11:07.3981811Z * [new tag] ciflow/trunk/141914 -> ciflow/trunk/141914 2025-03-04T20:11:07.3981970Z * [new tag] ciflow/trunk/141961 -> ciflow/trunk/141961 2025-03-04T20:11:07.3982103Z * [new tag] ciflow/trunk/142091 -> ciflow/trunk/142091 2025-03-04T20:11:07.3982221Z * [new tag] ciflow/trunk/142092 -> ciflow/trunk/142092 2025-03-04T20:11:07.3982349Z * [new tag] ciflow/trunk/142097 -> ciflow/trunk/142097 2025-03-04T20:11:07.3982526Z * [new tag] ciflow/trunk/142179 -> ciflow/trunk/142179 2025-03-04T20:11:07.3982954Z * [new tag] ciflow/trunk/142272 -> ciflow/trunk/142272 2025-03-04T20:11:07.3983157Z * [new tag] ciflow/trunk/142273 -> ciflow/trunk/142273 2025-03-04T20:11:07.3983290Z * [new tag] ciflow/trunk/142326 -> ciflow/trunk/142326 2025-03-04T20:11:07.3983420Z * [new tag] ciflow/trunk/142346 -> ciflow/trunk/142346 2025-03-04T20:11:07.3983553Z * [new tag] ciflow/trunk/142350 -> ciflow/trunk/142350 2025-03-04T20:11:07.3983692Z * [new tag] ciflow/trunk/142372 -> ciflow/trunk/142372 2025-03-04T20:11:07.3983822Z * [new tag] ciflow/trunk/142477 -> ciflow/trunk/142477 2025-03-04T20:11:07.3984081Z * [new tag] ciflow/trunk/142821 -> ciflow/trunk/142821 2025-03-04T20:11:07.3984248Z * [new tag] ciflow/trunk/142859 -> ciflow/trunk/142859 2025-03-04T20:11:07.3984535Z * [new tag] ciflow/trunk/142865 -> ciflow/trunk/142865 2025-03-04T20:11:07.3984723Z * [new tag] ciflow/trunk/143082 -> ciflow/trunk/143082 2025-03-04T20:11:07.3984857Z * [new tag] ciflow/trunk/143093 -> ciflow/trunk/143093 2025-03-04T20:11:07.3985136Z * [new tag] ciflow/trunk/143220 -> ciflow/trunk/143220 2025-03-04T20:11:07.3987937Z * [new tag] ciflow/trunk/143261 -> ciflow/trunk/143261 2025-03-04T20:11:07.3988260Z * [new tag] ciflow/trunk/143303 -> ciflow/trunk/143303 2025-03-04T20:11:07.3988404Z * [new tag] ciflow/trunk/143313 -> ciflow/trunk/143313 2025-03-04T20:11:07.3988529Z * [new tag] ciflow/trunk/143347 -> ciflow/trunk/143347 2025-03-04T20:11:07.3988661Z * [new tag] ciflow/trunk/143402 -> ciflow/trunk/143402 2025-03-04T20:11:07.3988807Z * [new tag] ciflow/trunk/143416 -> ciflow/trunk/143416 2025-03-04T20:11:07.3989106Z * [new tag] ciflow/trunk/143451 -> ciflow/trunk/143451 2025-03-04T20:11:07.3989392Z * [new tag] ciflow/trunk/143475 -> ciflow/trunk/143475 2025-03-04T20:11:07.3989781Z * [new tag] ciflow/trunk/143630 -> ciflow/trunk/143630 2025-03-04T20:11:07.3990062Z * [new tag] ciflow/trunk/143666 -> ciflow/trunk/143666 2025-03-04T20:11:07.3990489Z * [new tag] ciflow/trunk/143671 -> ciflow/trunk/143671 2025-03-04T20:11:07.3991267Z * [new tag] ciflow/trunk/143689 -> ciflow/trunk/143689 2025-03-04T20:11:07.3991408Z * [new tag] ciflow/trunk/143712 -> ciflow/trunk/143712 2025-03-04T20:11:07.3992327Z * [new tag] ciflow/trunk/143733 -> ciflow/trunk/143733 2025-03-04T20:11:07.3992475Z * [new tag] ciflow/trunk/143822 -> ciflow/trunk/143822 2025-03-04T20:11:07.3995242Z * [new tag] ciflow/trunk/143833 -> ciflow/trunk/143833 2025-03-04T20:11:07.3995408Z * [new tag] ciflow/trunk/143894 -> ciflow/trunk/143894 2025-03-04T20:11:07.3995719Z * [new tag] ciflow/trunk/143896 -> ciflow/trunk/143896 2025-03-04T20:11:07.3996279Z * [new tag] ciflow/trunk/143961 -> ciflow/trunk/143961 2025-03-04T20:11:07.3997041Z * [new tag] ciflow/trunk/143966 -> ciflow/trunk/143966 2025-03-04T20:11:07.3997472Z * [new tag] ciflow/trunk/144017 -> ciflow/trunk/144017 2025-03-04T20:11:07.3997655Z * [new tag] ciflow/trunk/144019 -> ciflow/trunk/144019 2025-03-04T20:11:07.3998501Z * [new tag] ciflow/trunk/144120 -> ciflow/trunk/144120 2025-03-04T20:11:07.3998656Z * [new tag] ciflow/trunk/144138 -> ciflow/trunk/144138 2025-03-04T20:11:07.3999101Z * [new tag] ciflow/trunk/144172 -> ciflow/trunk/144172 2025-03-04T20:11:07.3999496Z * [new tag] ciflow/trunk/144177 -> ciflow/trunk/144177 2025-03-04T20:11:07.4000024Z * [new tag] ciflow/trunk/144268 -> ciflow/trunk/144268 2025-03-04T20:11:07.4000264Z * [new tag] ciflow/trunk/144272 -> ciflow/trunk/144272 2025-03-04T20:11:07.4000401Z * [new tag] ciflow/trunk/144293 -> ciflow/trunk/144293 2025-03-04T20:11:07.4001359Z * [new tag] ciflow/trunk/144452 -> ciflow/trunk/144452 2025-03-04T20:11:07.4001874Z * [new tag] ciflow/trunk/144468 -> ciflow/trunk/144468 2025-03-04T20:11:07.4002098Z * [new tag] ciflow/trunk/144557 -> ciflow/trunk/144557 2025-03-04T20:11:07.4002245Z * [new tag] ciflow/trunk/144572 -> ciflow/trunk/144572 2025-03-04T20:11:07.4003288Z * [new tag] ciflow/trunk/144590 -> ciflow/trunk/144590 2025-03-04T20:11:07.4003817Z * [new tag] ciflow/trunk/144616 -> ciflow/trunk/144616 2025-03-04T20:11:07.4004030Z * [new tag] ciflow/trunk/144620 -> ciflow/trunk/144620 2025-03-04T20:11:07.4004282Z * [new tag] ciflow/trunk/144664 -> ciflow/trunk/144664 2025-03-04T20:11:07.4005244Z * [new tag] ciflow/trunk/144708 -> ciflow/trunk/144708 2025-03-04T20:11:07.4005844Z * [new tag] ciflow/trunk/144721 -> ciflow/trunk/144721 2025-03-04T20:11:07.4006000Z * [new tag] ciflow/trunk/144733 -> ciflow/trunk/144733 2025-03-04T20:11:07.4006123Z * [new tag] ciflow/trunk/144763 -> ciflow/trunk/144763 2025-03-04T20:11:07.4006877Z * [new tag] ciflow/trunk/144771 -> ciflow/trunk/144771 2025-03-04T20:11:07.4007032Z * [new tag] ciflow/trunk/144844 -> ciflow/trunk/144844 2025-03-04T20:11:07.4007167Z * [new tag] ciflow/trunk/144880 -> ciflow/trunk/144880 2025-03-04T20:11:07.4008032Z * [new tag] ciflow/trunk/144925 -> ciflow/trunk/144925 2025-03-04T20:11:07.4008475Z * [new tag] ciflow/trunk/144953 -> ciflow/trunk/144953 2025-03-04T20:11:07.4008882Z * [new tag] ciflow/trunk/144975 -> ciflow/trunk/144975 2025-03-04T20:11:07.4009496Z * [new tag] ciflow/trunk/144992 -> ciflow/trunk/144992 2025-03-04T20:11:07.4009642Z * [new tag] ciflow/trunk/145061 -> ciflow/trunk/145061 2025-03-04T20:11:07.4010627Z * [new tag] ciflow/trunk/145116 -> ciflow/trunk/145116 2025-03-04T20:11:07.4011348Z * [new tag] ciflow/trunk/145119 -> ciflow/trunk/145119 2025-03-04T20:11:07.4011765Z * [new tag] ciflow/trunk/145136 -> ciflow/trunk/145136 2025-03-04T20:11:07.4012274Z * [new tag] ciflow/trunk/145153 -> ciflow/trunk/145153 2025-03-04T20:11:07.4013226Z * [new tag] ciflow/trunk/145224 -> ciflow/trunk/145224 2025-03-04T20:11:07.4013660Z * [new tag] ciflow/trunk/145241 -> ciflow/trunk/145241 2025-03-04T20:11:07.4013848Z * [new tag] ciflow/trunk/145254 -> ciflow/trunk/145254 2025-03-04T20:11:07.4014372Z * [new tag] ciflow/trunk/145331 -> ciflow/trunk/145331 2025-03-04T20:11:07.4015120Z * [new tag] ciflow/trunk/145406 -> ciflow/trunk/145406 2025-03-04T20:11:07.4015550Z * [new tag] ciflow/trunk/145523 -> ciflow/trunk/145523 2025-03-04T20:11:07.4016173Z * [new tag] ciflow/trunk/145559 -> ciflow/trunk/145559 2025-03-04T20:11:07.4016576Z * [new tag] ciflow/trunk/145677 -> ciflow/trunk/145677 2025-03-04T20:11:07.4017620Z * [new tag] ciflow/trunk/145717 -> ciflow/trunk/145717 2025-03-04T20:11:07.4018270Z * [new tag] ciflow/trunk/145936 -> ciflow/trunk/145936 2025-03-04T20:11:07.4018472Z * [new tag] ciflow/trunk/145946 -> ciflow/trunk/145946 2025-03-04T20:11:07.4018814Z * [new tag] ciflow/trunk/145966 -> ciflow/trunk/145966 2025-03-04T20:11:07.4019204Z * [new tag] ciflow/trunk/145979 -> ciflow/trunk/145979 2025-03-04T20:11:07.4019412Z * [new tag] ciflow/trunk/146051 -> ciflow/trunk/146051 2025-03-04T20:11:07.4020635Z * [new tag] ciflow/trunk/146069 -> ciflow/trunk/146069 2025-03-04T20:11:07.4021115Z * [new tag] ciflow/trunk/146090 -> ciflow/trunk/146090 2025-03-04T20:11:07.4021505Z * [new tag] ciflow/trunk/146098 -> ciflow/trunk/146098 2025-03-04T20:11:07.4021955Z * [new tag] ciflow/trunk/146110 -> ciflow/trunk/146110 2025-03-04T20:11:07.4022367Z * [new tag] ciflow/trunk/146115 -> ciflow/trunk/146115 2025-03-04T20:11:07.4022557Z * [new tag] ciflow/trunk/146176 -> ciflow/trunk/146176 2025-03-04T20:11:07.4022676Z * [new tag] ciflow/trunk/146182 -> ciflow/trunk/146182 2025-03-04T20:11:07.4023501Z * [new tag] ciflow/trunk/146256 -> ciflow/trunk/146256 2025-03-04T20:11:07.4023972Z * [new tag] ciflow/trunk/146275 -> ciflow/trunk/146275 2025-03-04T20:11:07.4024371Z * [new tag] ciflow/trunk/146289 -> ciflow/trunk/146289 2025-03-04T20:11:07.4024801Z * [new tag] ciflow/trunk/146335 -> ciflow/trunk/146335 2025-03-04T20:11:07.4025185Z * [new tag] ciflow/trunk/146421 -> ciflow/trunk/146421 2025-03-04T20:11:07.4025393Z * [new tag] ciflow/trunk/146489 -> ciflow/trunk/146489 2025-03-04T20:11:07.4026516Z * [new tag] ciflow/trunk/146517 -> ciflow/trunk/146517 2025-03-04T20:11:07.4026697Z * [new tag] ciflow/trunk/146530 -> ciflow/trunk/146530 2025-03-04T20:11:07.4027035Z * [new tag] ciflow/trunk/146561 -> ciflow/trunk/146561 2025-03-04T20:11:07.4027438Z * [new tag] ciflow/trunk/146573 -> ciflow/trunk/146573 2025-03-04T20:11:07.4027827Z * [new tag] ciflow/trunk/146582 -> ciflow/trunk/146582 2025-03-04T20:11:07.4028368Z * [new tag] ciflow/trunk/146661 -> ciflow/trunk/146661 2025-03-04T20:11:07.4028613Z * [new tag] ciflow/trunk/146718 -> ciflow/trunk/146718 2025-03-04T20:11:07.4028735Z * [new tag] ciflow/trunk/146777 -> ciflow/trunk/146777 2025-03-04T20:11:07.4028855Z * [new tag] ciflow/trunk/146807 -> ciflow/trunk/146807 2025-03-04T20:11:07.4028974Z * [new tag] ciflow/trunk/146823 -> ciflow/trunk/146823 2025-03-04T20:11:07.4029158Z * [new tag] ciflow/trunk/146826 -> ciflow/trunk/146826 2025-03-04T20:11:07.4030122Z * [new tag] ciflow/trunk/146827 -> ciflow/trunk/146827 2025-03-04T20:11:07.4030654Z * [new tag] ciflow/trunk/146845 -> ciflow/trunk/146845 2025-03-04T20:11:07.4030849Z * [new tag] ciflow/trunk/146870 -> ciflow/trunk/146870 2025-03-04T20:11:07.4030980Z * [new tag] ciflow/trunk/146873 -> ciflow/trunk/146873 2025-03-04T20:11:07.4031102Z * [new tag] ciflow/trunk/146874 -> ciflow/trunk/146874 2025-03-04T20:11:07.4032410Z * [new tag] ciflow/trunk/146903 -> ciflow/trunk/146903 2025-03-04T20:11:07.4032595Z * [new tag] ciflow/trunk/146928 -> ciflow/trunk/146928 2025-03-04T20:11:07.4032713Z * [new tag] ciflow/trunk/146970 -> ciflow/trunk/146970 2025-03-04T20:11:07.4032840Z * [new tag] ciflow/trunk/147014 -> ciflow/trunk/147014 2025-03-04T20:11:07.4034278Z * [new tag] ciflow/trunk/147072 -> ciflow/trunk/147072 2025-03-04T20:11:07.4034822Z * [new tag] ciflow/trunk/147105 -> ciflow/trunk/147105 2025-03-04T20:11:07.4035230Z * [new tag] ciflow/trunk/147155 -> ciflow/trunk/147155 2025-03-04T20:11:07.4035702Z * [new tag] ciflow/trunk/147243 -> ciflow/trunk/147243 2025-03-04T20:11:07.4035892Z * [new tag] ciflow/trunk/147272 -> ciflow/trunk/147272 2025-03-04T20:11:07.4036022Z * [new tag] ciflow/trunk/147314 -> ciflow/trunk/147314 2025-03-04T20:11:07.4036667Z * [new tag] ciflow/trunk/147320 -> ciflow/trunk/147320 2025-03-04T20:11:07.4036875Z * [new tag] ciflow/trunk/147334 -> ciflow/trunk/147334 2025-03-04T20:11:07.4037316Z * [new tag] ciflow/trunk/147349 -> ciflow/trunk/147349 2025-03-04T20:11:07.4038071Z * [new tag] ciflow/trunk/147368 -> ciflow/trunk/147368 2025-03-04T20:11:07.4038461Z * [new tag] ciflow/trunk/147403 -> ciflow/trunk/147403 2025-03-04T20:11:07.4038920Z * [new tag] ciflow/trunk/147422 -> ciflow/trunk/147422 2025-03-04T20:11:07.4039867Z * [new tag] ciflow/trunk/147448 -> ciflow/trunk/147448 2025-03-04T20:11:07.4040298Z * [new tag] ciflow/trunk/147452 -> ciflow/trunk/147452 2025-03-04T20:11:07.4040733Z * [new tag] ciflow/trunk/147481 -> ciflow/trunk/147481 2025-03-04T20:11:07.4041136Z * [new tag] ciflow/trunk/147498 -> ciflow/trunk/147498 2025-03-04T20:11:07.4041333Z * [new tag] ciflow/trunk/147574 -> ciflow/trunk/147574 2025-03-04T20:11:07.4042121Z * [new tag] ciflow/trunk/147583 -> ciflow/trunk/147583 2025-03-04T20:11:07.4042613Z * [new tag] ciflow/trunk/147660 -> ciflow/trunk/147660 2025-03-04T20:11:07.4043031Z * [new tag] ciflow/trunk/147664 -> ciflow/trunk/147664 2025-03-04T20:11:07.4043179Z * [new tag] ciflow/trunk/147741 -> ciflow/trunk/147741 2025-03-04T20:11:07.4044039Z * [new tag] ciflow/trunk/147742 -> ciflow/trunk/147742 2025-03-04T20:11:07.4044235Z * [new tag] ciflow/trunk/147752 -> ciflow/trunk/147752 2025-03-04T20:11:07.4044355Z * [new tag] ciflow/trunk/147797 -> ciflow/trunk/147797 2025-03-04T20:11:07.4044482Z * [new tag] ciflow/trunk/147798 -> ciflow/trunk/147798 2025-03-04T20:11:07.4045641Z * [new tag] ciflow/trunk/147808 -> ciflow/trunk/147808 2025-03-04T20:11:07.4046070Z * [new tag] ciflow/trunk/147817 -> ciflow/trunk/147817 2025-03-04T20:11:07.4046625Z * [new tag] ciflow/trunk/147820 -> ciflow/trunk/147820 2025-03-04T20:11:07.4047284Z * [new tag] ciflow/trunk/147821 -> ciflow/trunk/147821 2025-03-04T20:11:07.4047717Z * [new tag] ciflow/trunk/147836 -> ciflow/trunk/147836 2025-03-04T20:11:07.4047929Z * [new tag] ciflow/trunk/147862 -> ciflow/trunk/147862 2025-03-04T20:11:07.4048326Z * [new tag] ciflow/trunk/147870 -> ciflow/trunk/147870 2025-03-04T20:11:07.4049172Z * [new tag] ciflow/trunk/147881 -> ciflow/trunk/147881 2025-03-04T20:11:07.4050028Z * [new tag] ciflow/trunk/147897 -> ciflow/trunk/147897 2025-03-04T20:11:07.4050720Z * [new tag] ciflow/trunk/147910 -> ciflow/trunk/147910 2025-03-04T20:11:07.4051177Z * [new tag] ciflow/trunk/147917 -> ciflow/trunk/147917 2025-03-04T20:11:07.4052066Z * [new tag] ciflow/trunk/147945 -> ciflow/trunk/147945 2025-03-04T20:11:07.4052489Z * [new tag] ciflow/trunk/147955 -> ciflow/trunk/147955 2025-03-04T20:11:07.4052648Z * [new tag] ciflow/trunk/147956 -> ciflow/trunk/147956 2025-03-04T20:11:07.4053280Z * [new tag] ciflow/trunk/147957 -> ciflow/trunk/147957 2025-03-04T20:11:07.4053482Z * [new tag] ciflow/trunk/147958 -> ciflow/trunk/147958 2025-03-04T20:11:07.4053926Z * [new tag] ciflow/trunk/147959 -> ciflow/trunk/147959 2025-03-04T20:11:07.4054374Z * [new tag] ciflow/trunk/147962 -> ciflow/trunk/147962 2025-03-04T20:11:07.4055626Z * [new tag] ciflow/trunk/147964 -> ciflow/trunk/147964 2025-03-04T20:11:07.4056075Z * [new tag] ciflow/trunk/147994 -> ciflow/trunk/147994 2025-03-04T20:11:07.4056532Z * [new tag] ciflow/trunk/147997 -> ciflow/trunk/147997 2025-03-04T20:11:07.4056710Z * [new tag] ciflow/trunk/148049 -> ciflow/trunk/148049 2025-03-04T20:11:07.4057374Z * [new tag] ciflow/trunk/148076 -> ciflow/trunk/148076 2025-03-04T20:11:07.4057555Z * [new tag] ciflow/trunk/148083 -> ciflow/trunk/148083 2025-03-04T20:11:07.4058020Z * [new tag] ciflow/trunk/148131 -> ciflow/trunk/148131 2025-03-04T20:11:07.4059285Z * [new tag] ciflow/trunk/148163 -> ciflow/trunk/148163 2025-03-04T20:11:07.4059757Z * [new tag] ciflow/trunk/148173 -> ciflow/trunk/148173 2025-03-04T20:11:07.4060146Z * [new tag] ciflow/trunk/148180 -> ciflow/trunk/148180 2025-03-04T20:11:07.4060568Z * [new tag] ciflow/trunk/148231 -> ciflow/trunk/148231 2025-03-04T20:11:07.4060923Z * [new tag] ciflow/trunk/148261 -> ciflow/trunk/148261 2025-03-04T20:11:07.4061103Z * [new tag] ciflow/trunk/148266 -> ciflow/trunk/148266 2025-03-04T20:11:07.4061509Z * [new tag] ciflow/trunk/148279 -> ciflow/trunk/148279 2025-03-04T20:11:07.4061892Z * [new tag] ciflow/trunk/148290 -> ciflow/trunk/148290 2025-03-04T20:11:07.4062050Z * [new tag] ciflow/trunk/148292 -> ciflow/trunk/148292 2025-03-04T20:11:07.4062663Z * [new tag] ciflow/trunk/148305 -> ciflow/trunk/148305 2025-03-04T20:11:07.4062802Z * [new tag] ciflow/trunk/148343 -> ciflow/trunk/148343 2025-03-04T20:11:07.4062925Z * [new tag] ciflow/trunk/148350 -> ciflow/trunk/148350 2025-03-04T20:11:07.4063340Z * [new tag] ciflow/trunk/148364 -> ciflow/trunk/148364 2025-03-04T20:11:07.4063763Z * [new tag] ciflow/trunk/148366 -> ciflow/trunk/148366 2025-03-04T20:11:07.4064134Z * [new tag] ciflow/trunk/148371 -> ciflow/trunk/148371 2025-03-04T20:11:07.4064605Z * [new tag] ciflow/trunk/148388 -> ciflow/trunk/148388 2025-03-04T20:11:07.4065009Z * [new tag] ciflow/trunk/148423 -> ciflow/trunk/148423 2025-03-04T20:11:07.4065187Z * [new tag] ciflow/trunk/70978 -> ciflow/trunk/70978 2025-03-04T20:11:07.4065785Z * [new tag] ciflow/trunk/70979 -> ciflow/trunk/70979 2025-03-04T20:11:07.4065945Z * [new tag] ciflow/unstable/123 -> ciflow/unstable/123 2025-03-04T20:11:07.4066085Z * [new tag] ciflow/unstable/146104 -> ciflow/unstable/146104 2025-03-04T20:11:07.4066223Z * [new tag] ciflow/unstable/146264 -> ciflow/unstable/146264 2025-03-04T20:11:07.4066357Z * [new tag] ciflow/unstable/147320 -> ciflow/unstable/147320 2025-03-04T20:11:07.4067392Z * [new tag] ciflow/xpu/137566 -> ciflow/xpu/137566 2025-03-04T20:11:07.4067530Z * [new tag] ciflow/xpu/137580 -> ciflow/xpu/137580 2025-03-04T20:11:07.4068084Z * [new tag] ciflow/xpu/138889 -> ciflow/xpu/138889 2025-03-04T20:11:07.4068236Z * [new tag] ciflow/xpu/138996 -> ciflow/xpu/138996 2025-03-04T20:11:07.4068359Z * [new tag] ciflow/xpu/139469 -> ciflow/xpu/139469 2025-03-04T20:11:07.4069951Z * [new tag] ciflow/xpu/139971 -> ciflow/xpu/139971 2025-03-04T20:11:07.4070431Z * [new tag] ciflow/xpu/140365 -> ciflow/xpu/140365 2025-03-04T20:11:07.4070572Z * [new tag] ciflow/xpu/140372 -> ciflow/xpu/140372 2025-03-04T20:11:07.4071056Z * [new tag] ciflow/xpu/140686 -> ciflow/xpu/140686 2025-03-04T20:11:07.4071567Z * [new tag] ciflow/xpu/140972 -> ciflow/xpu/140972 2025-03-04T20:11:07.4071961Z * [new tag] ciflow/xpu/142040 -> ciflow/xpu/142040 2025-03-04T20:11:07.4072159Z * [new tag] ciflow/xpu/142097 -> ciflow/xpu/142097 2025-03-04T20:11:07.4072990Z * [new tag] ciflow/xpu/143597 -> ciflow/xpu/143597 2025-03-04T20:11:07.4073479Z * [new tag] ciflow/xpu/143833 -> ciflow/xpu/143833 2025-03-04T20:11:07.4074374Z * [new tag] ciflow/xpu/144240 -> ciflow/xpu/144240 2025-03-04T20:11:07.4074786Z * [new tag] ciflow/xpu/144452 -> ciflow/xpu/144452 2025-03-04T20:11:07.4075186Z * [new tag] ciflow/xpu/144664 -> ciflow/xpu/144664 2025-03-04T20:11:07.4075596Z * [new tag] ciflow/xpu/146098 -> ciflow/xpu/146098 2025-03-04T20:11:07.4076080Z * [new tag] ciflow/xpu/147161 -> ciflow/xpu/147161 2025-03-04T20:11:07.4076483Z * [new tag] ciflow/xpu/147349 -> ciflow/xpu/147349 2025-03-04T20:11:07.4076911Z * [new tag] ciflow/xpu/147355 -> ciflow/xpu/147355 2025-03-04T20:11:07.4077800Z * [new tag] ciflow/xpu/147403 -> ciflow/xpu/147403 2025-03-04T20:11:07.4078216Z * [new tag] ciflow/xpu/147448 -> ciflow/xpu/147448 2025-03-04T20:11:07.4078875Z * [new tag] ciflow/xpu/147498 -> ciflow/xpu/147498 2025-03-04T20:11:07.4079692Z * [new tag] ciflow/xpu/147507 -> ciflow/xpu/147507 2025-03-04T20:11:07.4080435Z * [new tag] ciflow/xpu/147583 -> ciflow/xpu/147583 2025-03-04T20:11:07.4080958Z * [new tag] ciflow/xpu/147593 -> ciflow/xpu/147593 2025-03-04T20:11:07.4081364Z * [new tag] ciflow/xpu/147664 -> ciflow/xpu/147664 2025-03-04T20:11:07.4081774Z * [new tag] ciflow/xpu/147727 -> ciflow/xpu/147727 2025-03-04T20:11:07.4082161Z * [new tag] ciflow/xpu/147821 -> ciflow/xpu/147821 2025-03-04T20:11:07.4082409Z * [new tag] ciflow/xpu/147945 -> ciflow/xpu/147945 2025-03-04T20:11:07.4082835Z * [new tag] ciflow/xpu/147955 -> ciflow/xpu/147955 2025-03-04T20:11:07.4083578Z * [new tag] ciflow/xpu/147956 -> ciflow/xpu/147956 2025-03-04T20:11:07.4083996Z * [new tag] ciflow/xpu/147957 -> ciflow/xpu/147957 2025-03-04T20:11:07.4084499Z * [new tag] ciflow/xpu/147958 -> ciflow/xpu/147958 2025-03-04T20:11:07.4084899Z * [new tag] ciflow/xpu/147959 -> ciflow/xpu/147959 2025-03-04T20:11:07.4085045Z * [new tag] ciflow/xpu/147962 -> ciflow/xpu/147962 2025-03-04T20:11:07.4085468Z * [new tag] ciflow/xpu/148076 -> ciflow/xpu/148076 2025-03-04T20:11:07.4085864Z * [new tag] ciflow/xpu/148081 -> ciflow/xpu/148081 2025-03-04T20:11:07.4086058Z * [new tag] ciflow/xpu/148305 -> ciflow/xpu/148305 2025-03-04T20:11:07.4086193Z * [new tag] ciflow/xpu/148313 -> ciflow/xpu/148313 2025-03-04T20:11:07.4087020Z * [new tag] ciflow/xpu/148366 -> ciflow/xpu/148366 2025-03-04T20:11:07.4087164Z * [new tag] ciflow/xpu/148403 -> ciflow/xpu/148403 2025-03-04T20:11:07.4087288Z * [new tag] ciflow/xpu/148423 -> ciflow/xpu/148423 2025-03-04T20:11:07.4087723Z * [new tag] cslpull75 -> cslpull75 2025-03-04T20:11:07.4087876Z * [new tag] cslpull76 -> cslpull76 2025-03-04T20:11:07.4087998Z * [new tag] cslpull77 -> cslpull77 2025-03-04T20:11:07.4089411Z * [new tag] cslpull78 -> cslpull78 2025-03-04T20:11:07.4089873Z * [new tag] cslpull79 -> cslpull79 2025-03-04T20:11:07.4090044Z * [new tag] cslpull80 -> cslpull80 2025-03-04T20:11:07.4090635Z * [new tag] cslpull81 -> cslpull81 2025-03-04T20:11:07.4090759Z * [new tag] cslpull82 -> cslpull82 2025-03-04T20:11:07.4090878Z * [new tag] cslpull83 -> cslpull83 2025-03-04T20:11:07.4093121Z * [new tag] cslpull84 -> cslpull84 2025-03-04T20:11:07.4093609Z * [new tag] cslpull85 -> cslpull85 2025-03-04T20:11:07.4093732Z * [new tag] cslpull86 -> cslpull86 2025-03-04T20:11:07.4094715Z * [new tag] cslpull87 -> cslpull87 2025-03-04T20:11:07.4094873Z * [new tag] cslpull88 -> cslpull88 2025-03-04T20:11:07.4094996Z * [new tag] cslpull89 -> cslpull89 2025-03-04T20:11:07.4095994Z * [new tag] cslpull90 -> cslpull90 2025-03-04T20:11:07.4096166Z * [new tag] cslpull91 -> cslpull91 2025-03-04T20:11:07.4097085Z * [new tag] cslpull92 -> cslpull92 2025-03-04T20:11:07.4097271Z * [new tag] flight_5 -> flight_5 2025-03-04T20:11:07.4098094Z * [new tag] flight_5.1 -> flight_5.1 2025-03-04T20:11:07.4098233Z * [new tag] flight_5.2 -> flight_5.2 2025-03-04T20:11:07.4099365Z * [new tag] flight_5.3 -> flight_5.3 2025-03-04T20:11:07.4099874Z * [new tag] forpull1 -> forpull1 2025-03-04T20:11:07.4100352Z * [new tag] malfet/tag-2ef5611 -> malfet/tag-2ef5611 2025-03-04T20:11:07.4100558Z * [new tag] malfet/tag-317b1a0 -> malfet/tag-317b1a0 2025-03-04T20:11:07.4101411Z * [new tag] malfet/tag-ec6f767 -> malfet/tag-ec6f767 2025-03-04T20:11:07.4102016Z * [new tag] nightly-binary -> nightly-binary 2025-03-04T20:11:07.4102543Z * [new tag] sqzhang_flight4_plus -> sqzhang_flight4_plus 2025-03-04T20:11:07.4103445Z * [new tag] sqzhang_flight_3 -> sqzhang_flight_3 2025-03-04T20:11:07.4103845Z * [new tag] v0.1.1 -> v0.1.1 2025-03-04T20:11:07.4104055Z * [new tag] v0.1.10 -> v0.1.10 2025-03-04T20:11:07.4104819Z * [new tag] v0.1.11 -> v0.1.11 2025-03-04T20:11:07.4105019Z * [new tag] v0.1.12 -> v0.1.12 2025-03-04T20:11:07.4105705Z * [new tag] v0.1.2 -> v0.1.2 2025-03-04T20:11:07.4105900Z * [new tag] v0.1.3 -> v0.1.3 2025-03-04T20:11:07.4106018Z * [new tag] v0.1.4 -> v0.1.4 2025-03-04T20:11:07.4106912Z * [new tag] v0.1.5 -> v0.1.5 2025-03-04T20:11:07.4107517Z * [new tag] v0.1.6 -> v0.1.6 2025-03-04T20:11:07.4108226Z * [new tag] v0.1.7 -> v0.1.7 2025-03-04T20:11:07.4108383Z * [new tag] v0.1.8 -> v0.1.8 2025-03-04T20:11:07.4108884Z * [new tag] v0.1.9 -> v0.1.9 2025-03-04T20:11:07.4109384Z * [new tag] v0.2.0 -> v0.2.0 2025-03-04T20:11:07.4109522Z * [new tag] v0.3.0 -> v0.3.0 2025-03-04T20:11:07.4110107Z * [new tag] v0.3.1 -> v0.3.1 2025-03-04T20:11:07.4111036Z * [new tag] v0.4.0 -> v0.4.0 2025-03-04T20:11:07.4111752Z * [new tag] v0.4.1 -> v0.4.1 2025-03-04T20:11:07.4111943Z * [new tag] v1.0.0 -> v1.0.0 2025-03-04T20:11:07.4112660Z * [new tag] v1.0.0a0 -> v1.0.0a0 2025-03-04T20:11:07.4112841Z * [new tag] v1.0.1 -> v1.0.1 2025-03-04T20:11:07.4113690Z * [new tag] v1.0rc0 -> v1.0rc0 2025-03-04T20:11:07.4113936Z * [new tag] v1.0rc1 -> v1.0rc1 2025-03-04T20:11:07.4114062Z * [new tag] v1.1.0 -> v1.1.0 2025-03-04T20:11:07.4114900Z * [new tag] v1.1.0a0 -> v1.1.0a0 2025-03-04T20:11:07.4115054Z * [new tag] v1.10.0 -> v1.10.0 2025-03-04T20:11:07.4115188Z * [new tag] v1.10.0-rc1 -> v1.10.0-rc1 2025-03-04T20:11:07.4117285Z * [new tag] v1.10.0-rc2 -> v1.10.0-rc2 2025-03-04T20:11:07.4117661Z * [new tag] v1.10.0-rc3 -> v1.10.0-rc3 2025-03-04T20:11:07.4117788Z * [new tag] v1.10.1 -> v1.10.1 2025-03-04T20:11:07.4118905Z * [new tag] v1.10.1-rc1 -> v1.10.1-rc1 2025-03-04T20:11:07.4119483Z * [new tag] v1.10.2 -> v1.10.2 2025-03-04T20:11:07.4119848Z * [new tag] v1.10.2-rc1 -> v1.10.2-rc1 2025-03-04T20:11:07.4119988Z * [new tag] v1.11.0 -> v1.11.0 2025-03-04T20:11:07.4120098Z * [new tag] v1.11.0-rc1 -> v1.11.0-rc1 2025-03-04T20:11:07.4120883Z * [new tag] v1.11.0-rc2 -> v1.11.0-rc2 2025-03-04T20:11:07.4121055Z * [new tag] v1.11.0-rc3 -> v1.11.0-rc3 2025-03-04T20:11:07.4121721Z * [new tag] v1.11.0-rc4 -> v1.11.0-rc4 2025-03-04T20:11:07.4121891Z * [new tag] v1.11.0-rc5 -> v1.11.0-rc5 2025-03-04T20:11:07.4122009Z * [new tag] v1.11.0-rc6 -> v1.11.0-rc6 2025-03-04T20:11:07.4122935Z * [new tag] v1.11.0-rc7 -> v1.11.0-rc7 2025-03-04T20:11:07.4123059Z * [new tag] v1.12.0 -> v1.12.0 2025-03-04T20:11:07.4123166Z * [new tag] v1.12.0-rc1 -> v1.12.0-rc1 2025-03-04T20:11:07.4124492Z * [new tag] v1.12.0-rc2 -> v1.12.0-rc2 2025-03-04T20:11:07.4124621Z * [new tag] v1.12.0-rc3 -> v1.12.0-rc3 2025-03-04T20:11:07.4124740Z * [new tag] v1.12.0-rc4 -> v1.12.0-rc4 2025-03-04T20:11:07.4127439Z * [new tag] v1.12.0-rc5 -> v1.12.0-rc5 2025-03-04T20:11:07.4127878Z * [new tag] v1.12.0-rc6 -> v1.12.0-rc6 2025-03-04T20:11:07.4128207Z * [new tag] v1.12.0-rc7 -> v1.12.0-rc7 2025-03-04T20:11:07.4128378Z * [new tag] v1.12.0-rc8 -> v1.12.0-rc8 2025-03-04T20:11:07.4128517Z * [new tag] v1.12.1 -> v1.12.1 2025-03-04T20:11:07.4129406Z * [new tag] v1.12.1-rc1 -> v1.12.1-rc1 2025-03-04T20:11:07.4129908Z * [new tag] v1.12.1-rc2 -> v1.12.1-rc2 2025-03-04T20:11:07.4130079Z * [new tag] v1.12.1-rc3 -> v1.12.1-rc3 2025-03-04T20:11:07.4130671Z * [new tag] v1.12.1-rc4 -> v1.12.1-rc4 2025-03-04T20:11:07.4130797Z * [new tag] v1.12.1-rc5 -> v1.12.1-rc5 2025-03-04T20:11:07.4132133Z * [new tag] v1.13.0 -> v1.13.0 2025-03-04T20:11:07.4132279Z * [new tag] v1.13.0-rc1 -> v1.13.0-rc1 2025-03-04T20:11:07.4132394Z * [new tag] v1.13.0-rc2 -> v1.13.0-rc2 2025-03-04T20:11:07.4133433Z * [new tag] v1.13.0-rc3 -> v1.13.0-rc3 2025-03-04T20:11:07.4133565Z * [new tag] v1.13.0-rc4 -> v1.13.0-rc4 2025-03-04T20:11:07.4134355Z * [new tag] v1.13.0-rc5 -> v1.13.0-rc5 2025-03-04T20:11:07.4134562Z * [new tag] v1.13.0-rc6 -> v1.13.0-rc6 2025-03-04T20:11:07.4135521Z * [new tag] v1.13.1 -> v1.13.1 2025-03-04T20:11:07.4135718Z * [new tag] v1.13.1-rc1 -> v1.13.1-rc1 2025-03-04T20:11:07.4136204Z * [new tag] v1.2.0 -> v1.2.0 2025-03-04T20:11:07.4136628Z * [new tag] v1.2.0a0 -> v1.2.0a0 2025-03-04T20:11:07.4137141Z * [new tag] v1.3.0 -> v1.3.0 2025-03-04T20:11:07.4137600Z * [new tag] v1.3.0a0 -> v1.3.0a0 2025-03-04T20:11:07.4137776Z * [new tag] v1.3.1 -> v1.3.1 2025-03-04T20:11:07.4138412Z * [new tag] v1.4.0 -> v1.4.0 2025-03-04T20:11:07.4138614Z * [new tag] v1.4.0a0 -> v1.4.0a0 2025-03-04T20:11:07.4138739Z * [new tag] v1.4.1 -> v1.4.1 2025-03-04T20:11:07.4147403Z * [new tag] v1.5.0 -> v1.5.0 2025-03-04T20:11:07.4148242Z * [new tag] v1.5.0-rc1 -> v1.5.0-rc1 2025-03-04T20:11:07.4148452Z * [new tag] v1.5.0-rc2 -> v1.5.0-rc2 2025-03-04T20:11:07.4149138Z * [new tag] v1.5.0-rc3 -> v1.5.0-rc3 2025-03-04T20:11:07.4149304Z * [new tag] v1.5.0-rc4 -> v1.5.0-rc4 2025-03-04T20:11:07.4149409Z * [new tag] v1.5.0-rc5 -> v1.5.0-rc5 2025-03-04T20:11:07.4149798Z * [new tag] v1.5.1 -> v1.5.1 2025-03-04T20:11:07.4149928Z * [new tag] v1.5.1-rc1 -> v1.5.1-rc1 2025-03-04T20:11:07.4150407Z * [new tag] v1.6.0 -> v1.6.0 2025-03-04T20:11:07.4150558Z * [new tag] v1.6.0-rc1 -> v1.6.0-rc1 2025-03-04T20:11:07.4150910Z * [new tag] v1.6.0-rc2 -> v1.6.0-rc2 2025-03-04T20:11:07.4151261Z * [new tag] v1.6.0-rc3 -> v1.6.0-rc3 2025-03-04T20:11:07.4151429Z * [new tag] v1.6.0-rc4 -> v1.6.0-rc4 2025-03-04T20:11:07.4151535Z * [new tag] v1.6.0-rc5 -> v1.6.0-rc5 2025-03-04T20:11:07.4151866Z * [new tag] v1.6.0-rc6 -> v1.6.0-rc6 2025-03-04T20:11:07.4152175Z * [new tag] v1.6.0-rc7 -> v1.6.0-rc7 2025-03-04T20:11:07.4152337Z * [new tag] v1.7.0 -> v1.7.0 2025-03-04T20:11:07.4152458Z * [new tag] v1.7.0-rc1 -> v1.7.0-rc1 2025-03-04T20:11:07.4152958Z * [new tag] v1.7.0-rc2 -> v1.7.0-rc2 2025-03-04T20:11:07.4153152Z * [new tag] v1.7.0-rc3 -> v1.7.0-rc3 2025-03-04T20:11:07.4153302Z * [new tag] v1.7.0-rc4 -> v1.7.0-rc4 2025-03-04T20:11:07.4153716Z * [new tag] v1.7.1 -> v1.7.1 2025-03-04T20:11:07.4153905Z * [new tag] v1.7.1-rc1 -> v1.7.1-rc1 2025-03-04T20:11:07.4154280Z * [new tag] v1.7.1-rc2 -> v1.7.1-rc2 2025-03-04T20:11:07.4154641Z * [new tag] v1.7.1-rc3 -> v1.7.1-rc3 2025-03-04T20:11:07.4154780Z * [new tag] v1.8.0 -> v1.8.0 2025-03-04T20:11:07.4155108Z * [new tag] v1.8.0-rc1 -> v1.8.0-rc1 2025-03-04T20:11:07.4155658Z * [new tag] v1.8.0-rc2 -> v1.8.0-rc2 2025-03-04T20:11:07.4156157Z * [new tag] v1.8.0-rc3 -> v1.8.0-rc3 2025-03-04T20:11:07.4156270Z * [new tag] v1.8.0-rc4 -> v1.8.0-rc4 2025-03-04T20:11:07.4156370Z * [new tag] v1.8.0-rc5 -> v1.8.0-rc5 2025-03-04T20:11:07.4156475Z * [new tag] v1.8.1 -> v1.8.1 2025-03-04T20:11:07.4156837Z * [new tag] v1.8.1-rc1 -> v1.8.1-rc1 2025-03-04T20:11:07.4156991Z * [new tag] v1.8.1-rc2 -> v1.8.1-rc2 2025-03-04T20:11:07.4157095Z * [new tag] v1.8.1-rc3 -> v1.8.1-rc3 2025-03-04T20:11:07.4157202Z * [new tag] v1.8.2 -> v1.8.2 2025-03-04T20:11:07.4160674Z * [new tag] v1.8.2-rc1 -> v1.8.2-rc1 2025-03-04T20:11:07.4160790Z * [new tag] v1.9.0 -> v1.9.0 2025-03-04T20:11:07.4160894Z * [new tag] v1.9.0-rc1 -> v1.9.0-rc1 2025-03-04T20:11:07.4161012Z * [new tag] v1.9.0-rc2 -> v1.9.0-rc2 2025-03-04T20:11:07.4161114Z * [new tag] v1.9.0-rc3 -> v1.9.0-rc3 2025-03-04T20:11:07.4161222Z * [new tag] v1.9.0-rc4 -> v1.9.0-rc4 2025-03-04T20:11:07.4161324Z * [new tag] v1.9.1 -> v1.9.1 2025-03-04T20:11:07.4176307Z * [new tag] v1.9.1-rc1 -> v1.9.1-rc1 2025-03-04T20:11:07.4176693Z * [new tag] v1.9.1-rc2 -> v1.9.1-rc2 2025-03-04T20:11:07.4176856Z * [new tag] v2.0.0 -> v2.0.0 2025-03-04T20:11:07.4177246Z * [new tag] v2.0.0-rc1 -> v2.0.0-rc1 2025-03-04T20:11:07.4177390Z * [new tag] v2.0.0-rc2 -> v2.0.0-rc2 2025-03-04T20:11:07.4177746Z * [new tag] v2.0.0-rc3 -> v2.0.0-rc3 2025-03-04T20:11:07.4178413Z * [new tag] v2.0.0-rc4 -> v2.0.0-rc4 2025-03-04T20:11:07.4178586Z * [new tag] v2.0.0-rc5 -> v2.0.0-rc5 2025-03-04T20:11:07.4178708Z * [new tag] v2.0.0-rc6 -> v2.0.0-rc6 2025-03-04T20:11:07.4179071Z * [new tag] v2.0.1 -> v2.0.1 2025-03-04T20:11:07.4179222Z * [new tag] v2.0.1-rc1 -> v2.0.1-rc1 2025-03-04T20:11:07.4179612Z * [new tag] v2.0.1-rc2 -> v2.0.1-rc2 2025-03-04T20:11:07.4180266Z * [new tag] v2.0.1-rc3 -> v2.0.1-rc3 2025-03-04T20:11:07.4180793Z * [new tag] v2.0.1-rc4 -> v2.0.1-rc4 2025-03-04T20:11:07.4180962Z * [new tag] v2.1.0 -> v2.1.0 2025-03-04T20:11:07.4181620Z * [new tag] v2.1.0-rc1 -> v2.1.0-rc1 2025-03-04T20:11:07.4182050Z * [new tag] v2.1.0-rc2 -> v2.1.0-rc2 2025-03-04T20:11:07.4182199Z * [new tag] v2.1.0-rc3 -> v2.1.0-rc3 2025-03-04T20:11:07.4182519Z * [new tag] v2.1.0-rc4 -> v2.1.0-rc4 2025-03-04T20:11:07.4182720Z * [new tag] v2.1.0-rc5 -> v2.1.0-rc5 2025-03-04T20:11:07.4183061Z * [new tag] v2.1.0-rc6 -> v2.1.0-rc6 2025-03-04T20:11:07.4183229Z * [new tag] v2.1.1 -> v2.1.1 2025-03-04T20:11:07.4183358Z * [new tag] v2.1.1-rc1 -> v2.1.1-rc1 2025-03-04T20:11:07.4183746Z * [new tag] v2.1.1-rc2 -> v2.1.1-rc2 2025-03-04T20:11:07.4183884Z * [new tag] v2.1.1-rc3 -> v2.1.1-rc3 2025-03-04T20:11:07.4184211Z * [new tag] v2.1.1-rc4 -> v2.1.1-rc4 2025-03-04T20:11:07.4184573Z * [new tag] v2.1.1-rc5 -> v2.1.1-rc5 2025-03-04T20:11:07.4184764Z * [new tag] v2.1.1-rc6 -> v2.1.1-rc6 2025-03-04T20:11:07.4184871Z * [new tag] v2.1.2 -> v2.1.2 2025-03-04T20:11:07.4185257Z * [new tag] v2.1.2-rc1 -> v2.1.2-rc1 2025-03-04T20:11:07.4185470Z * [new tag] v2.1.2-rc2 -> v2.1.2-rc2 2025-03-04T20:11:07.4185879Z * [new tag] v2.1.2-rc3 -> v2.1.2-rc3 2025-03-04T20:11:07.4186012Z * [new tag] v2.2.0 -> v2.2.0 2025-03-04T20:11:07.4186417Z * [new tag] v2.2.0-rc1 -> v2.2.0-rc1 2025-03-04T20:11:07.4186552Z * [new tag] v2.2.0-rc2 -> v2.2.0-rc2 2025-03-04T20:11:07.4187166Z * [new tag] v2.2.0-rc3 -> v2.2.0-rc3 2025-03-04T20:11:07.4187883Z * [new tag] v2.2.0-rc4 -> v2.2.0-rc4 2025-03-04T20:11:07.4188023Z * [new tag] v2.2.0-rc5 -> v2.2.0-rc5 2025-03-04T20:11:07.4188282Z * [new tag] v2.2.0-rc6 -> v2.2.0-rc6 2025-03-04T20:11:07.4188660Z * [new tag] v2.2.0-rc7 -> v2.2.0-rc7 2025-03-04T20:11:07.4188807Z * [new tag] v2.2.0-rc8 -> v2.2.0-rc8 2025-03-04T20:11:07.4189118Z * [new tag] v2.2.1 -> v2.2.1 2025-03-04T20:11:07.4189287Z * [new tag] v2.2.1-rc1 -> v2.2.1-rc1 2025-03-04T20:11:07.4189649Z * [new tag] v2.2.1-rc2 -> v2.2.1-rc2 2025-03-04T20:11:07.4189774Z * [new tag] v2.2.1-rc3 -> v2.2.1-rc3 2025-03-04T20:11:07.4190085Z * [new tag] v2.2.2 -> v2.2.2 2025-03-04T20:11:07.4190244Z * [new tag] v2.2.2-rc1 -> v2.2.2-rc1 2025-03-04T20:11:07.4190683Z * [new tag] v2.2.2-rc2 -> v2.2.2-rc2 2025-03-04T20:11:07.4190838Z * [new tag] v2.2.2-rc3 -> v2.2.2-rc3 2025-03-04T20:11:07.4191147Z * [new tag] v2.3.0 -> v2.3.0 2025-03-04T20:11:07.4191313Z * [new tag] v2.3.0-rc1 -> v2.3.0-rc1 2025-03-04T20:11:07.4191452Z * [new tag] v2.3.0-rc10 -> v2.3.0-rc10 2025-03-04T20:11:07.4191792Z * [new tag] v2.3.0-rc11 -> v2.3.0-rc11 2025-03-04T20:11:07.4191963Z * [new tag] v2.3.0-rc12 -> v2.3.0-rc12 2025-03-04T20:11:07.4192384Z * [new tag] v2.3.0-rc2 -> v2.3.0-rc2 2025-03-04T20:11:07.4192565Z * [new tag] v2.3.0-rc3 -> v2.3.0-rc3 2025-03-04T20:11:07.4192921Z * [new tag] v2.3.0-rc4 -> v2.3.0-rc4 2025-03-04T20:11:07.4193071Z * [new tag] v2.3.0-rc5 -> v2.3.0-rc5 2025-03-04T20:11:07.4193804Z * [new tag] v2.3.0-rc6 -> v2.3.0-rc6 2025-03-04T20:11:07.4193937Z * [new tag] v2.3.0-rc7 -> v2.3.0-rc7 2025-03-04T20:11:07.4194042Z * [new tag] v2.3.0-rc8 -> v2.3.0-rc8 2025-03-04T20:11:07.4194161Z * [new tag] v2.3.0-rc9 -> v2.3.0-rc9 2025-03-04T20:11:07.4194548Z * [new tag] v2.3.1 -> v2.3.1 2025-03-04T20:11:07.4194709Z * [new tag] v2.3.1-rc1 -> v2.3.1-rc1 2025-03-04T20:11:07.4194820Z * [new tag] v2.3.1-rc2 -> v2.3.1-rc2 2025-03-04T20:11:07.4195227Z * [new tag] v2.3.1-rc3 -> v2.3.1-rc3 2025-03-04T20:11:07.4195346Z * [new tag] v2.4.0 -> v2.4.0 2025-03-04T20:11:07.4195664Z * [new tag] v2.4.0-rc1 -> v2.4.0-rc1 2025-03-04T20:11:07.4195809Z * [new tag] v2.4.0-rc2 -> v2.4.0-rc2 2025-03-04T20:11:07.4195929Z * [new tag] v2.4.0-rc3 -> v2.4.0-rc3 2025-03-04T20:11:07.4196693Z * [new tag] v2.4.0-rc4 -> v2.4.0-rc4 2025-03-04T20:11:07.4196923Z * [new tag] v2.4.0-rc5 -> v2.4.0-rc5 2025-03-04T20:11:07.4197380Z * [new tag] v2.4.0-rc6 -> v2.4.0-rc6 2025-03-04T20:11:07.4197506Z * [new tag] v2.4.0-rc7 -> v2.4.0-rc7 2025-03-04T20:11:07.4197939Z * [new tag] v2.4.0-rc8 -> v2.4.0-rc8 2025-03-04T20:11:07.4198088Z * [new tag] v2.4.0-rc9 -> v2.4.0-rc9 2025-03-04T20:11:07.4198486Z * [new tag] v2.4.1 -> v2.4.1 2025-03-04T20:11:07.4198620Z * [new tag] v2.4.1-rc1 -> v2.4.1-rc1 2025-03-04T20:11:07.4199027Z * [new tag] v2.4.1-rc2 -> v2.4.1-rc2 2025-03-04T20:11:07.4199173Z * [new tag] v2.4.1-rc3 -> v2.4.1-rc3 2025-03-04T20:11:07.4200575Z * [new tag] v2.5.0 -> v2.5.0 2025-03-04T20:11:07.4200695Z * [new tag] v2.5.0-rc1 -> v2.5.0-rc1 2025-03-04T20:11:07.4200817Z * [new tag] v2.5.0-rc10 -> v2.5.0-rc10 2025-03-04T20:11:07.4202354Z * [new tag] v2.5.0-rc2 -> v2.5.0-rc2 2025-03-04T20:11:07.4202481Z * [new tag] v2.5.0-rc3 -> v2.5.0-rc3 2025-03-04T20:11:07.4203162Z * [new tag] v2.5.0-rc4 -> v2.5.0-rc4 2025-03-04T20:11:07.4203532Z * [new tag] v2.5.0-rc5 -> v2.5.0-rc5 2025-03-04T20:11:07.4203645Z * [new tag] v2.5.0-rc6 -> v2.5.0-rc6 2025-03-04T20:11:07.4204566Z * [new tag] v2.5.0-rc7 -> v2.5.0-rc7 2025-03-04T20:11:07.4204731Z * [new tag] v2.5.0-rc8 -> v2.5.0-rc8 2025-03-04T20:11:07.4205121Z * [new tag] v2.5.0-rc9 -> v2.5.0-rc9 2025-03-04T20:11:07.4205538Z * [new tag] v2.5.1 -> v2.5.1 2025-03-04T20:11:07.4206198Z * [new tag] v2.5.1-rc1 -> v2.5.1-rc1 2025-03-04T20:11:07.4206334Z * [new tag] v2.6.0 -> v2.6.0 2025-03-04T20:11:07.4206735Z * [new tag] v2.6.0-rc1 -> v2.6.0-rc1 2025-03-04T20:11:07.4207724Z * [new tag] v2.6.0-rc2 -> v2.6.0-rc2 2025-03-04T20:11:07.4207855Z * [new tag] v2.6.0-rc3 -> v2.6.0-rc3 2025-03-04T20:11:07.4208870Z * [new tag] v2.6.0-rc4 -> v2.6.0-rc4 2025-03-04T20:11:07.4209356Z * [new tag] v2.6.0-rc5 -> v2.6.0-rc5 2025-03-04T20:11:07.4211256Z * [new tag] v2.6.0-rc6 -> v2.6.0-rc6 2025-03-04T20:11:07.4211372Z * [new tag] v2.6.0-rc7 -> v2.6.0-rc7 2025-03-04T20:11:07.4212071Z * [new tag] v2.6.0-rc8 -> v2.6.0-rc8 2025-03-04T20:11:07.4212258Z * [new tag] v2.6.0-rc9 -> v2.6.0-rc9 2025-03-04T20:11:07.4212739Z * [new tag] whc_flight_1 -> whc_flight_1 2025-03-04T20:11:07.4213121Z * [new tag] whc_flight_2 -> whc_flight_2 2025-03-04T20:11:07.4592051Z * [new tag] whc_flight_4 -> whc_flight_4 2025-03-04T20:11:07.4598363Z [command]/usr/bin/git rev-parse --verify --quiet 1b7498080987913ecb3aff6253c5e88f3540d911^{object} 2025-03-04T20:11:07.4622786Z 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:11:07.4626938Z ##[endgroup] 2025-03-04T20:11:07.4627433Z ##[group]Determining the checkout info 2025-03-04T20:11:07.4627719Z ##[endgroup] 2025-03-04T20:11:07.4634021Z [command]/usr/bin/git sparse-checkout disable 2025-03-04T20:11:07.4682427Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig 2025-03-04T20:11:07.4708970Z ##[group]Checking out the ref 2025-03-04T20:11:07.4719343Z [command]/usr/bin/git checkout --progress --force 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:11:08.3409571Z Note: switching to '1b7498080987913ecb3aff6253c5e88f3540d911'. 2025-03-04T20:11:08.3410885Z 2025-03-04T20:11:08.3412325Z You are in 'detached HEAD' state. You can look around, make experimental 2025-03-04T20:11:08.3412821Z changes and commit them, and you can discard any commits you make in this 2025-03-04T20:11:08.3413262Z state without impacting any branches by switching back to a branch. 2025-03-04T20:11:08.3413806Z 2025-03-04T20:11:08.3414419Z If you want to create a new branch to retain commits you create, you may 2025-03-04T20:11:08.3415086Z do so (now or later) by using -c with the switch command. Example: 2025-03-04T20:11:08.3415475Z 2025-03-04T20:11:08.3415636Z git switch -c 2025-03-04T20:11:08.3415873Z 2025-03-04T20:11:08.3416018Z Or undo this operation with: 2025-03-04T20:11:08.3416235Z 2025-03-04T20:11:08.3416364Z git switch - 2025-03-04T20:11:08.3416529Z 2025-03-04T20:11:08.3416828Z Turn off this advice by setting config variable advice.detachedHead to false 2025-03-04T20:11:08.3417213Z 2025-03-04T20:11:08.3417603Z HEAD is now at 1b749808098 Update on "[dynamo] remove internal stack trace for fullgraph=True graph breaks" 2025-03-04T20:11:08.3453770Z ##[endgroup] 2025-03-04T20:11:08.3454143Z ##[group]Setting up auth for fetching submodules 2025-03-04T20:11:08.3460923Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-03-04T20:11:08.3520892Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2025-03-04T20:11:08.3549613Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2025-03-04T20:11:08.3572426Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2025-03-04T20:11:08.3605525Z ##[endgroup] 2025-03-04T20:11:08.3610491Z ##[group]Fetching submodules 2025-03-04T20:11:08.3610799Z [command]/usr/bin/git submodule sync --recursive 2025-03-04T20:11:08.3899508Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2025-03-04T20:11:08.4192481Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2025-03-04T20:11:08.4193142Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2025-03-04T20:11:08.4405261Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2025-03-04T20:11:08.4405920Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2025-03-04T20:11:08.4406505Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX' 2025-03-04T20:11:08.4407221Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2025-03-04T20:11:08.4407935Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2025-03-04T20:11:08.4424372Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2025-03-04T20:11:08.4425166Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel' 2025-03-04T20:11:08.4425895Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2025-03-04T20:11:08.4426566Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2025-03-04T20:11:08.4427242Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2025-03-04T20:11:08.4428159Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2025-03-04T20:11:08.4439784Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen' 2025-03-04T20:11:08.4446540Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2025-03-04T20:11:08.4447204Z Submodule 'third_party/flash-attention' (https://github.com/Dao-AILab/flash-attention.git) registered for path 'third_party/flash-attention' 2025-03-04T20:11:08.4447860Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2025-03-04T20:11:08.4448428Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2025-03-04T20:11:08.4459463Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2025-03-04T20:11:08.4460540Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo' 2025-03-04T20:11:08.4465197Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2025-03-04T20:11:08.4467558Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2025-03-04T20:11:08.4468086Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2025-03-04T20:11:08.4468886Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2025-03-04T20:11:08.4477275Z Submodule 'third_party/kleidiai' (https://github.com/ARM-software/kleidiai.git) registered for path 'third_party/kleidiai' 2025-03-04T20:11:08.4490661Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2025-03-04T20:11:08.4491474Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2025-03-04T20:11:08.4492080Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2025-03-04T20:11:08.4492828Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2025-03-04T20:11:08.4496947Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2025-03-04T20:11:08.4533185Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2025-03-04T20:11:08.4533924Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2025-03-04T20:11:08.4534720Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2025-03-04T20:11:08.4535445Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2025-03-04T20:11:08.4536116Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2025-03-04T20:11:08.4536705Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2025-03-04T20:11:08.4537352Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2025-03-04T20:11:08.4575436Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2025-03-04T20:11:08.7103601Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2025-03-04T20:11:08.7105019Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2025-03-04T20:11:08.7105851Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2025-03-04T20:11:08.7126608Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2025-03-04T20:11:08.8731326Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2025-03-04T20:11:08.8732060Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'... 2025-03-04T20:11:08.8732689Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2025-03-04T20:11:08.8733415Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NVTX'... 2025-03-04T20:11:08.8817733Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2025-03-04T20:11:09.9397298Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'... 2025-03-04T20:11:09.9398196Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2025-03-04T20:11:09.9399142Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2025-03-04T20:11:09.9399984Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'... 2025-03-04T20:11:09.9400810Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kleidiai'... 2025-03-04T20:11:09.9401581Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 2025-03-04T20:11:09.9402419Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2025-03-04T20:11:09.9403291Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention'... 2025-03-04T20:11:09.9404371Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2025-03-04T20:11:09.9405188Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2025-03-04T20:11:09.9405768Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2025-03-04T20:11:09.9406352Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2025-03-04T20:11:10.0398554Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2025-03-04T20:11:10.9740486Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'... 2025-03-04T20:11:10.9741431Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2025-03-04T20:11:10.9742243Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'... 2025-03-04T20:11:10.9743049Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'... 2025-03-04T20:11:10.9743721Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2025-03-04T20:11:11.0741957Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2025-03-04T20:11:21.2372249Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'... 2025-03-04T20:11:21.2374858Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2025-03-04T20:11:21.2375395Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/composable_kernel'... 2025-03-04T20:11:21.2375877Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'... 2025-03-04T20:11:21.2376367Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2025-03-04T20:11:21.2376831Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2025-03-04T20:11:21.2377265Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/eigen'... 2025-03-04T20:11:21.2377696Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2025-03-04T20:11:21.2507412Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2025-03-04T20:11:21.2612977Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2025-03-04T20:11:21.2698375Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2025-03-04T20:11:21.2936823Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2025-03-04T20:11:21.3208651Z Submodule path 'third_party/NVTX': checked out 'e170594ac7cf1dac584da473d4ca9301087090c1' 2025-03-04T20:11:21.3520665Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191' 2025-03-04T20:11:21.8784249Z Submodule path 'third_party/XNNPACK': checked out '51a0103656eff6fc9bfd39a4597923c4b542c883' 2025-03-04T20:11:21.9023302Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415' 2025-03-04T20:11:22.0909458Z Submodule path 'third_party/composable_kernel': checked out '8086bbe3a78d931eb96fe12fdc014082e18d18d3' 2025-03-04T20:11:22.1318071Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006' 2025-03-04T20:11:22.2201293Z Submodule path 'third_party/cpuinfo': checked out '1e83a2fdd3102f65c6f1fb602c1b320486218a99' 2025-03-04T20:11:22.2502522Z Submodule path 'third_party/cudnn_frontend': checked out '91b7532f3386768bba4f444ee7672b497f34da8a' 2025-03-04T20:11:22.7358992Z Submodule path 'third_party/cutlass': checked out 'afa1772203677c5118fcd82537a9c8fefbcc7008' 2025-03-04T20:11:22.9726120Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c' 2025-03-04T20:11:23.0647573Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e' 2025-03-04T20:11:23.0667494Z Submodule 'third_party/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/third_party/asmjit' 2025-03-04T20:11:23.0668289Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/third_party/cpuinfo' 2025-03-04T20:11:23.0672012Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/fbgemm/third_party/cutlass' 2025-03-04T20:11:23.0672814Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/third_party/googletest' 2025-03-04T20:11:23.0675591Z Submodule 'third_party/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/third_party/hipify_torch' 2025-03-04T20:11:23.0719852Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/asmjit'... 2025-03-04T20:11:24.1377921Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/hipify_torch'... 2025-03-04T20:11:24.1378740Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cpuinfo'... 2025-03-04T20:11:24.2194315Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cutlass'... 2025-03-04T20:11:25.4245175Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/googletest'... 2025-03-04T20:11:25.4789109Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81' 2025-03-04T20:11:25.5649532Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3' 2025-03-04T20:11:25.8915846Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6' 2025-03-04T20:11:25.9525782Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796' 2025-03-04T20:11:25.9646377Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1' 2025-03-04T20:11:26.0334674Z Submodule path 'third_party/flash-attention': checked out '979702c87a8713a8e0a5e9fee122b90d2ef13be5' 2025-03-04T20:11:26.0350856Z Submodule 'csrc/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/flash-attention/csrc/composable_kernel' 2025-03-04T20:11:26.0351640Z Submodule 'csrc/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/flash-attention/csrc/cutlass' 2025-03-04T20:11:26.0381204Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/composable_kernel'... 2025-03-04T20:11:28.4843615Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/cutlass'... 2025-03-04T20:11:28.6846153Z Submodule path 'third_party/flash-attention/csrc/composable_kernel': checked out '888317e698e9803c62bd38568abc9e05d7709f33' 2025-03-04T20:11:29.1864068Z Submodule path 'third_party/flash-attention/csrc/cutlass': checked out 'c506e16788cb08416a4a57e11a9067beeee29420' 2025-03-04T20:11:29.2983134Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b' 2025-03-04T20:11:29.3293299Z Submodule path 'third_party/fmt': checked out '123913715afeb8a437e6388b4473fcc4753e1c9a' 2025-03-04T20:11:29.3669013Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2025-03-04T20:11:29.3915277Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509' 2025-03-04T20:11:29.4379916Z Submodule path 'third_party/googletest': checked out 'b514bdc898e2951020cbdca1304b75f5950d1f59' 2025-03-04T20:11:29.4498915Z Submodule path 'third_party/ideep': checked out 'e026f3b0318087fe19e2b062e8edf55bfe7a522c' 2025-03-04T20:11:29.4515182Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2025-03-04T20:11:29.4535879Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2025-03-04T20:11:43.9491648Z Submodule path 'third_party/ideep/mkl-dnn': checked out '66f0cb9eb66affd2da3bf5f8d897376f04aae6af' 2025-03-04T20:11:43.9664816Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42' 2025-03-04T20:11:44.0551403Z Submodule path 'third_party/kineto': checked out 'a054a4be0db117c579a21747debf19c863631f26' 2025-03-04T20:11:44.0579628Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2025-03-04T20:11:44.0580534Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2025-03-04T20:11:44.0581385Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2025-03-04T20:11:44.0597409Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2025-03-04T20:11:45.0260344Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2025-03-04T20:11:45.4636944Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2025-03-04T20:11:45.5569505Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2025-03-04T20:11:45.5584598Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-03-04T20:11:45.5585456Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-03-04T20:11:45.5588455Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-03-04T20:11:45.5589671Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-03-04T20:11:45.5590497Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-03-04T20:11:45.5594993Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-03-04T20:11:45.5595905Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-03-04T20:11:45.5602505Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-03-04T20:11:45.5626649Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2025-03-04T20:11:46.7024129Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2025-03-04T20:11:46.7032932Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2025-03-04T20:11:46.7039128Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2025-03-04T20:11:46.7040155Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2025-03-04T20:11:46.8027373Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2025-03-04T20:11:46.9286247Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2025-03-04T20:11:47.0291376Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2025-03-04T20:11:52.3726507Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2025-03-04T20:11:52.3905672Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2025-03-04T20:11:52.4226610Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2025-03-04T20:11:52.4329020Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2025-03-04T20:11:52.4350032Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-03-04T20:11:52.4380343Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2025-03-04T20:11:52.8240508Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2025-03-04T20:11:52.8397214Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2025-03-04T20:11:52.8797662Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2025-03-04T20:11:52.9658679Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2025-03-04T20:11:52.9828187Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2025-03-04T20:11:53.0142107Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164' 2025-03-04T20:11:53.0679956Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2025-03-04T20:11:53.1019499Z Submodule path 'third_party/kleidiai': checked out 'ef685a13cfbe8d418aa2ed34350e21e4938358b6' 2025-03-04T20:11:53.1338983Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78' 2025-03-04T20:11:53.2361222Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806' 2025-03-04T20:11:53.5248755Z Submodule path 'third_party/onnx': checked out 'b8baa8446686496da4cc8fda09f2b6fe65c2a02c' 2025-03-04T20:11:53.5295278Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2025-03-04T20:11:53.5320737Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2025-03-04T20:11:54.4498059Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '3e9dfa2866941655c56877882565e7577de6fc7b' 2025-03-04T20:11:54.5019242Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878' 2025-03-04T20:11:54.5035753Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-03-04T20:11:54.5036503Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest' 2025-03-04T20:11:54.5037493Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-03-04T20:11:54.5039493Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-03-04T20:11:54.5040562Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-03-04T20:11:54.5041513Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-03-04T20:11:54.5044703Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-03-04T20:11:54.5045549Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-03-04T20:11:54.5157087Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'... 2025-03-04T20:11:54.9483477Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentelemetry-proto'... 2025-03-04T20:11:54.9484266Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentracing-cpp'... 2025-03-04T20:11:54.9484951Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/ms-gsl'... 2025-03-04T20:11:54.9485638Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp'... 2025-03-04T20:11:55.0484748Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/googletest'... 2025-03-04T20:11:55.9383642Z 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fmt/config remote.origin.url 2025-03-04T20:12:11.4850004Z Entering 'third_party/gemmlowp/gemmlowp' 2025-03-04T20:12:11.4898341Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gemmlowp/gemmlowp/config remote.origin.url 2025-03-04T20:12:11.4912677Z Entering 'third_party/gloo' 2025-03-04T20:12:11.4958471Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gloo/config remote.origin.url 2025-03-04T20:12:11.4974270Z Entering 'third_party/googletest' 2025-03-04T20:12:11.5023255Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config remote.origin.url 2025-03-04T20:12:11.5039329Z Entering 'third_party/ideep' 2025-03-04T20:12:11.5092686Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config remote.origin.url 2025-03-04T20:12:11.5107278Z Entering 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/googletest/config remote.origin.url 2025-03-04T20:12:11.5923746Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-03-04T20:12:11.5977586Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/json/config remote.origin.url 2025-03-04T20:12:11.6004375Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-03-04T20:12:11.6081541Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/pfs/config remote.origin.url 2025-03-04T20:12:11.6096255Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-03-04T20:12:11.6165903Z 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config remote.origin.url 2025-03-04T20:12:11.6456063Z Entering 'third_party/onnx' 2025-03-04T20:12:11.6502691Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/config remote.origin.url 2025-03-04T20:12:11.6561513Z Entering 'third_party/onnx/third_party/pybind11' 2025-03-04T20:12:11.6619018Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/pybind11/config remote.origin.url 2025-03-04T20:12:11.6705065Z Entering 'third_party/opentelemetry-cpp' 2025-03-04T20:12:11.6748630Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/config remote.origin.url 2025-03-04T20:12:11.6764965Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-03-04T20:12:11.6815526Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/benchmark/config remote.origin.url 2025-03-04T20:12:11.6826654Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-03-04T20:12:11.6871537Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/googletest/config remote.origin.url 2025-03-04T20:12:11.6887562Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-03-04T20:12:11.6930786Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/ms-gsl/config remote.origin.url 2025-03-04T20:12:11.6945447Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-03-04T20:12:11.6995241Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/nlohmann-json/config remote.origin.url 2025-03-04T20:12:11.7012598Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-03-04T20:12:11.7053406Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentelemetry-proto/config remote.origin.url 2025-03-04T20:12:11.7064627Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-03-04T20:12:11.7116055Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentracing-cpp/config remote.origin.url 2025-03-04T20:12:11.7131272Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-03-04T20:12:11.7216841Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/config remote.origin.url 2025-03-04T20:12:11.7230075Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-03-04T20:12:11.7280519Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/civetweb/config remote.origin.url 2025-03-04T20:12:11.7293601Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-03-04T20:12:11.7348256Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/googletest/config remote.origin.url 2025-03-04T20:12:11.7363709Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-03-04T20:12:11.7444086Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/tools/vcpkg/config remote.origin.url 2025-03-04T20:12:11.7483467Z Entering 'third_party/pocketfft' 2025-03-04T20:12:11.7523784Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config remote.origin.url 2025-03-04T20:12:11.7535251Z Entering 'third_party/protobuf' 2025-03-04T20:12:11.7582542Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config remote.origin.url 2025-03-04T20:12:11.7603112Z Entering 'third_party/protobuf/third_party/benchmark' 2025-03-04T20:12:11.7654517Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config remote.origin.url 2025-03-04T20:12:11.7694416Z Entering 'third_party/protobuf/third_party/googletest' 2025-03-04T20:12:11.7715611Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config remote.origin.url 2025-03-04T20:12:11.7752989Z Entering 'third_party/psimd' 2025-03-04T20:12:11.7796038Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config remote.origin.url 2025-03-04T20:12:11.7810511Z Entering 'third_party/pthreadpool' 2025-03-04T20:12:11.7860058Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/pthreadpool/config remote.origin.url 2025-03-04T20:12:11.7876847Z Entering 'third_party/pybind11' 2025-03-04T20:12:11.7928656Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pybind11/config remote.origin.url 2025-03-04T20:12:11.7942741Z Entering 'third_party/python-peachpy' 2025-03-04T20:12:11.7985698Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-peachpy/config remote.origin.url 2025-03-04T20:12:11.8001784Z Entering 'third_party/sleef' 2025-03-04T20:12:11.8044378Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/sleef/config remote.origin.url 2025-03-04T20:12:11.8061132Z Entering 'third_party/tensorpipe' 2025-03-04T20:12:11.8107839Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/config remote.origin.url 2025-03-04T20:12:11.8122589Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-03-04T20:12:11.8171724Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config remote.origin.url 2025-03-04T20:12:11.8184069Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-03-04T20:12:11.8232474Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config remote.origin.url 2025-03-04T20:12:11.8249548Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-03-04T20:12:11.8310596Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config remote.origin.url 2025-03-04T20:12:11.8326705Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-03-04T20:12:11.8382219Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config remote.origin.url 2025-03-04T20:12:11.8397902Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-03-04T20:12:11.8448221Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/modules/tools/clang/config remote.origin.url 2025-03-04T20:12:11.9308380Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:' 2025-03-04T20:12:11.9607306Z Entering 'android/libs/fbjni' 2025-03-04T20:12:11.9645259Z Entering 'third_party/FP16' 2025-03-04T20:12:11.9681226Z Entering 'third_party/FXdiv' 2025-03-04T20:12:11.9743870Z Entering 'third_party/NNPACK' 2025-03-04T20:12:11.9760997Z Entering 'third_party/NVTX' 2025-03-04T20:12:11.9810936Z Entering 'third_party/VulkanMemoryAllocator' 2025-03-04T20:12:11.9852088Z Entering 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Entering 'third_party/tensorpipe' 2025-03-04T20:12:12.2726055Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-03-04T20:12:12.2763715Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-03-04T20:12:12.2800610Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-03-04T20:12:12.2841849Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-03-04T20:12:12.2875558Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-03-04T20:12:12.2939884Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2025-03-04T20:12:12.3304860Z Entering 'android/libs/fbjni' 2025-03-04T20:12:12.3346655Z Entering 'third_party/FP16' 2025-03-04T20:12:12.3382042Z Entering 'third_party/FXdiv' 2025-03-04T20:12:12.3443195Z Entering 'third_party/NNPACK' 2025-03-04T20:12:12.3457596Z Entering 'third_party/NVTX' 2025-03-04T20:12:12.3528456Z Entering 'third_party/VulkanMemoryAllocator' 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2025-03-04T20:12:12.5230237Z Entering 'third_party/kleidiai' 2025-03-04T20:12:12.5269543Z Entering 'third_party/mimalloc' 2025-03-04T20:12:12.5319052Z Entering 'third_party/nlohmann' 2025-03-04T20:12:12.5361039Z Entering 'third_party/onnx' 2025-03-04T20:12:12.5410209Z Entering 'third_party/onnx/third_party/pybind11' 2025-03-04T20:12:12.5467011Z Entering 'third_party/opentelemetry-cpp' 2025-03-04T20:12:12.5511402Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-03-04T20:12:12.5548432Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-03-04T20:12:12.5584489Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-03-04T20:12:12.5633219Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-03-04T20:12:12.5665638Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-03-04T20:12:12.5699769Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-03-04T20:12:12.5736220Z Entering 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Entering 'third_party/tensorpipe' 2025-03-04T20:12:12.6372009Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-03-04T20:12:12.6372452Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-03-04T20:12:12.6412667Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-03-04T20:12:12.6453737Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-03-04T20:12:12.6487067Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-03-04T20:12:12.6543798Z ##[endgroup] 2025-03-04T20:12:12.6625595Z [command]/usr/bin/git log -1 --format=%H 2025-03-04T20:12:12.6626050Z 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:12:12.6908580Z Prepare all required actions 2025-03-04T20:12:12.6909238Z Getting action download info 2025-03-04T20:12:12.8340115Z ##[group]Run ./.github/actions/setup-linux 2025-03-04T20:12:12.8340423Z env: 2025-03-04T20:12:12.8340647Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:12.8340894Z ##[endgroup] 2025-03-04T20:12:12.8405263Z ##[group]Run set -euo pipefail 2025-03-04T20:12:12.8405568Z set -euo pipefail 2025-03-04T20:12:12.8405821Z function get_ec2_metadata() { 2025-03-04T20:12:12.8406101Z  # Pulled from instance metadata endpoint for EC2 2025-03-04T20:12:12.8406533Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2025-03-04T20:12:12.8406928Z  category=$1 2025-03-04T20:12:12.8407216Z  # If it is GCP runner (runner name contains gcp), do not run this 2025-03-04T20:12:12.8407541Z  runner_name_str=i-07188c9acbdc11b95 2025-03-04T20:12:12.8407859Z  if [[ -f /.inarc ]]; then 2025-03-04T20:12:12.8408130Z  echo "ARC Runner, no info on ec2 metadata" 2025-03-04T20:12:12.8408418Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2025-03-04T20:12:12.8408751Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2025-03-04T20:12:12.8409055Z  else 2025-03-04T20:12:12.8409638Z  curl -H "X-aws-ec2-metadata-token: $(curl -s -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 30")" -fsSL "http://169.254.169.254/latest/meta-data/${category}" 2025-03-04T20:12:12.8410347Z  fi 2025-03-04T20:12:12.8410535Z } 2025-03-04T20:12:12.8410758Z echo "ami-id: $(get_ec2_metadata ami-id)" 2025-03-04T20:12:12.8411071Z echo "instance-id: $(get_ec2_metadata instance-id)" 2025-03-04T20:12:12.8411389Z echo "instance-type: $(get_ec2_metadata instance-type)" 2025-03-04T20:12:12.8411718Z echo "system info $(uname -a)" 2025-03-04T20:12:12.8417768Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:12.8418071Z env: 2025-03-04T20:12:12.8418272Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:12.8418497Z ##[endgroup] 2025-03-04T20:12:12.8574375Z ami-id: ami-05b10e08d247fb927 2025-03-04T20:12:12.8692340Z instance-id: i-07188c9acbdc11b95 2025-03-04T20:12:12.8780740Z instance-type: m7i-flex.8xlarge 2025-03-04T20:12:12.8790820Z system info Linux ip-10-0-30-106.ec2.internal 6.1.128-136.201.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Mon Feb 10 16:18:01 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux 2025-03-04T20:12:12.8816046Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T20:12:12.8816659Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T20:12:12.8821302Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:12.8821584Z env: 2025-03-04T20:12:12.8821757Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:12.8821957Z ##[endgroup] 2025-03-04T20:12:12.8885888Z ##[group]Run if systemctl is-active --quiet docker; then 2025-03-04T20:12:12.8886227Z if systemctl is-active --quiet docker; then 2025-03-04T20:12:12.8886509Z  echo "Docker daemon is running..."; 2025-03-04T20:12:12.8887376Z else 2025-03-04T20:12:12.8887659Z  echo "Starting docker deamon..." && sudo systemctl start docker; 2025-03-04T20:12:12.8887987Z fi 2025-03-04T20:12:12.8896933Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:12.8897226Z env: 2025-03-04T20:12:12.8897422Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:12.8897640Z ##[endgroup] 2025-03-04T20:12:12.8974791Z Docker daemon is running... 2025-03-04T20:12:12.9036109Z ##[group]Run nick-fields/retry@v3.0.0 2025-03-04T20:12:12.9036370Z with: 2025-03-04T20:12:12.9036559Z shell: bash 2025-03-04T20:12:12.9039766Z timeout_minutes: 5 2025-03-04T20:12:12.9040018Z max_attempts: 3 2025-03-04T20:12:12.9040232Z retry_wait_seconds: 30 2025-03-04T20:12:12.9041634Z command: AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\") aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" # For LF Runners we need to make sure we also login to Meta's ECR docker registry too. META_AWS_ACCOUNT_ID=308535385114 if [ "$AWS_ACCOUNT_ID" != "$META_AWS_ACCOUNT_ID" ] ; then aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ --password-stdin "$META_AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" fi 2025-03-04T20:12:12.9042971Z polling_interval_seconds: 1 2025-03-04T20:12:12.9043193Z warning_on_retry: true 2025-03-04T20:12:12.9043399Z continue_on_error: false 2025-03-04T20:12:12.9043591Z env: 2025-03-04T20:12:12.9043771Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:12.9043976Z AWS_RETRY_MODE: standard 2025-03-04T20:12:12.9044179Z AWS_MAX_ATTEMPTS: 5 2025-03-04T20:12:12.9044382Z AWS_DEFAULT_REGION: us-east-1 2025-03-04T20:12:12.9044594Z ##[endgroup] 2025-03-04T20:12:14.4857553Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-03-04T20:12:14.4858088Z Configure a credential helper to remove this warning. See 2025-03-04T20:12:14.4858601Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-03-04T20:12:14.4859253Z 2025-03-04T20:12:14.4859349Z Login Succeeded 2025-03-04T20:12:15.1133493Z Command completed after 1 attempt(s). 2025-03-04T20:12:15.1190154Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-03-04T20:12:15.1190579Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-03-04T20:12:15.1190909Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-03-04T20:12:15.1196523Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:15.1196789Z env: 2025-03-04T20:12:15.1196973Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:15.1197174Z ##[endgroup] 2025-03-04T20:12:15.1294193Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-03-04T20:12:15.1294618Z # ignore expansion of "docker ps -q" since it could be empty 2025-03-04T20:12:15.1294915Z # shellcheck disable=SC2046 2025-03-04T20:12:15.1295185Z docker stop $(docker ps -q) || true 2025-03-04T20:12:15.1295436Z # Prune all of the docker images 2025-03-04T20:12:15.1295675Z docker system prune -af 2025-03-04T20:12:15.1300759Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:15.1301020Z env: 2025-03-04T20:12:15.1301199Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:15.1301394Z ##[endgroup] 2025-03-04T20:12:15.1581080Z "docker stop" requires at least 1 argument. 2025-03-04T20:12:15.1581448Z See 'docker stop --help'. 2025-03-04T20:12:15.1581637Z 2025-03-04T20:12:15.1581784Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2025-03-04T20:12:15.1581985Z 2025-03-04T20:12:15.1582092Z Stop one or more running containers 2025-03-04T20:12:15.1908231Z Total reclaimed space: 0B 2025-03-04T20:12:15.1953514Z ##[group]Run set +e 2025-03-04T20:12:15.1953772Z set +e 2025-03-04T20:12:15.1953959Z set -x 2025-03-04T20:12:15.1954138Z  2025-03-04T20:12:15.1954332Z PT_DOMAIN=download.pytorch.org 2025-03-04T20:12:15.1954743Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2025-03-04T20:12:15.1955210Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2025-03-04T20:12:15.1956193Z # one is returned at random 2025-03-04T20:12:15.1956481Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2025-03-04T20:12:15.1956738Z  2025-03-04T20:12:15.1957066Z if [ -z "${RESOLVED_IP}" ]; then 2025-03-04T20:12:15.1957368Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2025-03-04T20:12:15.1957708Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2025-03-04T20:12:15.1957974Z  2025-03-04T20:12:15.1958160Z  if [ -z "${RESOLVED_IP}" ]; then 2025-03-04T20:12:15.1958777Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2025-03-04T20:12:15.1959050Z  exit 1 2025-03-04T20:12:15.1959481Z  fi 2025-03-04T20:12:15.1959659Z fi 2025-03-04T20:12:15.1959827Z  2025-03-04T20:12:15.1960258Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2025-03-04T20:12:15.1960522Z  # Clean up any old records first 2025-03-04T20:12:15.1961012Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2025-03-04T20:12:15.1961250Z fi 2025-03-04T20:12:15.1961414Z  2025-03-04T20:12:15.1961829Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2025-03-04T20:12:15.1962105Z cat /etc/hosts 2025-03-04T20:12:15.1968022Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:15.1968295Z env: 2025-03-04T20:12:15.1968482Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:15.1968691Z ##[endgroup] 2025-03-04T20:12:15.1994431Z + PT_DOMAIN=download.pytorch.org 2025-03-04T20:12:15.2004888Z ++ tail -n1 2025-03-04T20:12:15.2005212Z ++ dig -4 +short download.pytorch.org 2025-03-04T20:12:15.5617410Z + RESOLVED_IP=18.160.10.22 2025-03-04T20:12:15.5617724Z + '[' -z 18.160.10.22 ']' 2025-03-04T20:12:15.5617961Z + grep -r download.pytorch.org /etc/hosts 2025-03-04T20:12:15.5628986Z + echo '18.160.10.22 download.pytorch.org' 2025-03-04T20:12:15.5630538Z + sudo tee -a /etc/hosts 2025-03-04T20:12:15.7875043Z 18.160.10.22 download.pytorch.org 2025-03-04T20:12:15.7893739Z + cat /etc/hosts 2025-03-04T20:12:15.7899594Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2025-03-04T20:12:15.7905124Z ::1 localhost6 localhost6.localdomain6 2025-03-04T20:12:15.7905429Z 18.160.10.22 download.pytorch.org 2025-03-04T20:12:15.8227995Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main 2025-03-04T20:12:15.8228335Z with: 2025-03-04T20:12:15.8228815Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:15.8229355Z docker-build-dir: .ci/docker 2025-03-04T20:12:15.8229565Z working-directory: . 2025-03-04T20:12:15.8229815Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:15.8230084Z force-push: false 2025-03-04T20:12:15.8230262Z env: 2025-03-04T20:12:15.8230433Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:15.8230626Z ##[endgroup] 2025-03-04T20:12:15.8254978Z ##[group]Run set -ex 2025-03-04T20:12:15.8255269Z set -ex 2025-03-04T20:12:15.8255443Z  2025-03-04T20:12:15.8255729Z # If the docker build directory or the build script doesn't exist, the action will 2025-03-04T20:12:15.8256214Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2025-03-04T20:12:15.8256587Z # job could then download the pre-built image as usual 2025-03-04T20:12:15.8256949Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then 2025-03-04T20:12:15.8257271Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:15.8257591Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:15.8257872Z  2025-03-04T20:12:15.8258137Z  echo "There is no Docker build script in ${REPO_NAME} repo, skipping..." 2025-03-04T20:12:15.8258427Z  exit 0 2025-03-04T20:12:15.8258611Z else 2025-03-04T20:12:15.8258812Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:15.8259048Z fi 2025-03-04T20:12:15.8259241Z  2025-03-04T20:12:15.8259488Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2025-03-04T20:12:15.8259882Z  # The docker image name already includes the ECR prefix and tag, so we can just 2025-03-04T20:12:15.8260234Z  # use it as it is, but first let's extract the tag 2025-03-04T20:12:15.8260559Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2025-03-04T20:12:15.8260900Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:15.8261227Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:15.8261496Z else 2025-03-04T20:12:15.8261729Z  DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2025-03-04T20:12:15.8262040Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:15.8262450Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:15.8262808Z fi 2025-03-04T20:12:15.8270801Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:15.8271073Z env: 2025-03-04T20:12:15.8271256Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:15.8271462Z REPO_NAME: pytorch 2025-03-04T20:12:15.8272585Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:15.8273257Z DOCKER_BUILD_DIR: .ci/docker 2025-03-04T20:12:15.8273531Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:15.8273801Z ##[endgroup] 2025-03-04T20:12:15.8297727Z + [[ ! -d .ci/docker ]] 2025-03-04T20:12:15.8298023Z + [[ ! -f .ci/docker/build.sh ]] 2025-03-04T20:12:15.8298329Z + echo skip=false 2025-03-04T20:12:15.8298996Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e == *\3\0\8\5\3\5\3\8\5\1\1\4\.\d\k\r\.\e\c\r\.\u\s\-\e\a\s\t\-\1\.\a\m\a\z\o\n\a\w\s\.\c\o\m\/\p\y\t\o\r\c\h* ]] 2025-03-04T20:12:15.8307243Z ++ awk -F '[:,]' '{print $2}' 2025-03-04T20:12:15.8308106Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:15.8330222Z + DOCKER_TAG=e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:15.8334024Z + echo docker-tag=e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:15.8335015Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:15.8368613Z ##[group]Run set +e 2025-03-04T20:12:15.8368885Z set +e 2025-03-04T20:12:15.8369071Z set -x 2025-03-04T20:12:15.8369247Z  2025-03-04T20:12:15.8369419Z login() { 2025-03-04T20:12:15.8369750Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-03-04T20:12:15.8370081Z } 2025-03-04T20:12:15.8370263Z  2025-03-04T20:12:15.8370433Z retry () { 2025-03-04T20:12:15.8370642Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-03-04T20:12:15.8370867Z } 2025-03-04T20:12:15.8371030Z  2025-03-04T20:12:15.8371204Z retry login "${DOCKER_REGISTRY}" 2025-03-04T20:12:15.8371423Z  2025-03-04T20:12:15.8371605Z START_TIME=$(date +%s) 2025-03-04T20:12:15.8372274Z # Wait up to 120 minutes 2025-03-04T20:12:15.8372545Z while [[ $(( $(date +%s) - 7200 )) -lt $START_TIME ]]; do 2025-03-04T20:12:15.8372891Z  # Check if image already exists, if it does then skip building it 2025-03-04T20:12:15.8373347Z  if docker manifest inspect "${DOCKER_IMAGE}"; then 2025-03-04T20:12:15.8373622Z  exit 0 2025-03-04T20:12:15.8373821Z  fi 2025-03-04T20:12:15.8374007Z  2025-03-04T20:12:15.8374301Z  # NB: This flag is used by Docker build workflow to push the image to ECR, so we can 2025-03-04T20:12:15.8374749Z  # use this to differentiate between the Docker build and regular build jobs. For the 2025-03-04T20:12:15.8375168Z  # latter, it will wait for the Docker images to become available before continuing 2025-03-04T20:12:15.8375536Z  if [ "${DOCKER_PUSH:-false}" == "true" ]; then 2025-03-04T20:12:15.8375827Z  # It's a Docker build job, let's build the image 2025-03-04T20:12:15.8376068Z  break 2025-03-04T20:12:15.8376253Z  else 2025-03-04T20:12:15.8376504Z  # It's a regular build job, wait for the image to become available 2025-03-04T20:12:15.8376782Z  sleep 300 2025-03-04T20:12:15.8376973Z  fi 2025-03-04T20:12:15.8377146Z done 2025-03-04T20:12:15.8377316Z  2025-03-04T20:12:15.8377568Z # NB: This part requires a full checkout. Otherwise, the merge base will 2025-03-04T20:12:15.8377952Z # be empty. The default action would be to continue rebuild the image 2025-03-04T20:12:15.8378290Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2025-03-04T20:12:15.8378597Z  # if we're on the base branch then use the parent commit 2025-03-04T20:12:15.8378881Z  MERGE_BASE=$(git rev-parse HEAD~) 2025-03-04T20:12:15.8379111Z else 2025-03-04T20:12:15.8379506Z  # otherwise we're on a PR, so use the most recent base commit 2025-03-04T20:12:15.8379834Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2025-03-04T20:12:15.8380088Z fi 2025-03-04T20:12:15.8380261Z  2025-03-04T20:12:15.8380449Z if [[ -z "${MERGE_BASE}" ]]; then 2025-03-04T20:12:15.8380711Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:15.8380951Z  2025-03-04T20:12:15.8381276Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2025-03-04T20:12:15.8381710Z  exit 0 2025-03-04T20:12:15.8381897Z fi 2025-03-04T20:12:15.8382065Z  2025-03-04T20:12:15.8382294Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2025-03-04T20:12:15.8382730Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2025-03-04T20:12:15.8383115Z  exit 1 2025-03-04T20:12:15.8383295Z fi 2025-03-04T20:12:15.8383465Z  2025-03-04T20:12:15.8383726Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2025-03-04T20:12:15.8384149Z # If no image exists but the hash is the same as the previous hash then we should error out here 2025-03-04T20:12:15.8384535Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2025-03-04T20:12:15.8384943Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2025-03-04T20:12:15.8385400Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2025-03-04T20:12:15.8385686Z fi 2025-03-04T20:12:15.8385845Z  2025-03-04T20:12:15.8386037Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:15.8390669Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:15.8390930Z env: 2025-03-04T20:12:15.8391106Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:15.8391305Z DOCKER_BUILD_DIR: .ci/docker 2025-03-04T20:12:15.8391553Z BASE_REVISION: 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:12:15.8392094Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:15.8392618Z DOCKER_TAG: e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:15.8392915Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:15.8393190Z DOCKER_PUSH: 2025-03-04T20:12:15.8393367Z ##[endgroup] 2025-03-04T20:12:15.8413292Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:15.8413645Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:15.8439668Z + aws ecr get-login-password --region us-east-1 2025-03-04T20:12:15.8444392Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:16.3281141Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-03-04T20:12:16.3281865Z Configure a credential helper to remove this warning. See 2025-03-04T20:12:16.3283122Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-03-04T20:12:16.3283513Z 2025-03-04T20:12:16.3283627Z Login Succeeded 2025-03-04T20:12:16.3311437Z ++ date +%s 2025-03-04T20:12:16.3313916Z + START_TIME=1741119136 2025-03-04T20:12:16.3342523Z ++ date +%s 2025-03-04T20:12:16.3344752Z + [[ 1741111936 -lt 1741119136 ]] 2025-03-04T20:12:16.3345450Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:16.5435790Z { 2025-03-04T20:12:16.5436073Z "schemaVersion": 2, 2025-03-04T20:12:16.5436469Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2025-03-04T20:12:16.5436826Z "config": { 2025-03-04T20:12:16.5437526Z "mediaType": "application/vnd.docker.container.image.v1+json", 2025-03-04T20:12:16.5437833Z "size": 42070, 2025-03-04T20:12:16.5438163Z "digest": "sha256:9eba596d1817cef362b8eee5b1a58c3110c752e1c111809994c788e5df204b1e" 2025-03-04T20:12:16.5438504Z }, 2025-03-04T20:12:16.5438686Z "layers": [ 2025-03-04T20:12:16.5438872Z { 2025-03-04T20:12:16.5439134Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5439439Z "size": 30440118, 2025-03-04T20:12:16.5439874Z "digest": "sha256:8f84a9f2102e97a4a6bf673b150fc9894df5acc9618ad3484c6c36f768c1caa0" 2025-03-04T20:12:16.5440457Z }, 2025-03-04T20:12:16.5440768Z { 2025-03-04T20:12:16.5441029Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5441515Z "size": 1894, 2025-03-04T20:12:16.5441850Z "digest": "sha256:546bd73ecfd84e7a8723205acc797488833a44ca24cc0c25bb4a642fd2667f85" 2025-03-04T20:12:16.5442180Z }, 2025-03-04T20:12:16.5442348Z { 2025-03-04T20:12:16.5442608Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5442898Z "size": 319404697, 2025-03-04T20:12:16.5443213Z "digest": "sha256:4ba5b16f22b030e00b386b41fe894de1df517e23c8ba07ef6dfb9196a5698b7d" 2025-03-04T20:12:16.5443544Z }, 2025-03-04T20:12:16.5443711Z { 2025-03-04T20:12:16.5443959Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5444256Z "size": 864, 2025-03-04T20:12:16.5444558Z "digest": "sha256:64de9d43f178279dec12e258b22e9ef47d316393fabbc0cd1a4912a8d0815070" 2025-03-04T20:12:16.5444883Z }, 2025-03-04T20:12:16.5445045Z { 2025-03-04T20:12:16.5445294Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5445585Z "size": 106, 2025-03-04T20:12:16.5445885Z "digest": "sha256:db0a277d2bad9ea270266b2209b210e03d7943fdd0e281d26ba2602c3ba392af" 2025-03-04T20:12:16.5446211Z }, 2025-03-04T20:12:16.5446374Z { 2025-03-04T20:12:16.5446618Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5446910Z "size": 704, 2025-03-04T20:12:16.5447211Z "digest": "sha256:70c60a789cc0da5305c8d4abb20617b35dc7e1f9c07988f57bb373e37c08294b" 2025-03-04T20:12:16.5447530Z }, 2025-03-04T20:12:16.5447693Z { 2025-03-04T20:12:16.5447938Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5448232Z "size": 1216, 2025-03-04T20:12:16.5448534Z "digest": "sha256:25cadc1cd3aad6be708772f38bf0236b0658ce0fab2392354a86078c405c26d2" 2025-03-04T20:12:16.5448864Z }, 2025-03-04T20:12:16.5449026Z { 2025-03-04T20:12:16.5449328Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5449685Z "size": 485, 2025-03-04T20:12:16.5449989Z "digest": "sha256:374d8cd6d29fd9af63924ff454faf0e4122fe2d8c0ce854a85bb2bc6c111ec6c" 2025-03-04T20:12:16.5450322Z }, 2025-03-04T20:12:16.5450482Z { 2025-03-04T20:12:16.5450726Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5451025Z "size": 110341876, 2025-03-04T20:12:16.5451345Z "digest": "sha256:bcb2ba3f25a9c3ac7f4571fee0b939e1c77d500f6ea1047726ede05abd72df78" 2025-03-04T20:12:16.5451679Z }, 2025-03-04T20:12:16.5451842Z { 2025-03-04T20:12:16.5452085Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5452376Z "size": 4180, 2025-03-04T20:12:16.5452687Z "digest": "sha256:fbe64186fd30f6eea8c73fd9de7d86af228d9c64e4ef944a9b187ceaa7809d08" 2025-03-04T20:12:16.5453156Z }, 2025-03-04T20:12:16.5453332Z { 2025-03-04T20:12:16.5453691Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5454003Z "size": 1860, 2025-03-04T20:12:16.5454319Z "digest": "sha256:a4b05775098ed2bc34309b98a4e7e91f5857374bc060cb29f4e5bbf6264a15f5" 2025-03-04T20:12:16.5454653Z }, 2025-03-04T20:12:16.5454824Z { 2025-03-04T20:12:16.5455086Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5455483Z "size": 700, 2025-03-04T20:12:16.5455801Z "digest": "sha256:8d1cf83bb67fdebcda602a033c508ed2e543bf42e81f61df50b0e481a4d9b8cd" 2025-03-04T20:12:16.5456147Z }, 2025-03-04T20:12:16.5456316Z { 2025-03-04T20:12:16.5456572Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5456876Z "size": 477, 2025-03-04T20:12:16.5457190Z "digest": "sha256:8dfef80cec656f1902a5beae7e2452a246ba8af6899f5f2144211642acef4b30" 2025-03-04T20:12:16.5457531Z }, 2025-03-04T20:12:16.5457688Z { 2025-03-04T20:12:16.5457938Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5458305Z "size": 2816726602, 2025-03-04T20:12:16.5458635Z "digest": "sha256:18cc35ebce84d4de1e3853e530c0876eaf2e0034444663f025602c8d1666ff6b" 2025-03-04T20:12:16.5458971Z }, 2025-03-04T20:12:16.5459144Z { 2025-03-04T20:12:16.5459401Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5459704Z "size": 32, 2025-03-04T20:12:16.5460026Z "digest": 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"sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-03-04T20:12:16.5525907Z }, 2025-03-04T20:12:16.5526075Z { 2025-03-04T20:12:16.5526322Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5526615Z "size": 161, 2025-03-04T20:12:16.5526967Z "digest": "sha256:67823c8824491bbcf498f0a827acd0289152643ea7b5906383d22171357f3951" 2025-03-04T20:12:16.5527297Z }, 2025-03-04T20:12:16.5527462Z { 2025-03-04T20:12:16.5527711Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5528002Z "size": 765, 2025-03-04T20:12:16.5528309Z "digest": "sha256:c196c67b067c2da2bbd1ef57296cb4a7549b0574f323f572d8ae6ce6c0d89dde" 2025-03-04T20:12:16.5528646Z }, 2025-03-04T20:12:16.5528810Z { 2025-03-04T20:12:16.5529059Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5529355Z "size": 700, 2025-03-04T20:12:16.5529664Z "digest": "sha256:8d1cf83bb67fdebcda602a033c508ed2e543bf42e81f61df50b0e481a4d9b8cd" 2025-03-04T20:12:16.5529998Z }, 2025-03-04T20:12:16.5530164Z { 2025-03-04T20:12:16.5530413Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5530711Z "size": 139, 2025-03-04T20:12:16.5531027Z "digest": "sha256:3347939b4ff440cbabd3f7b1cfa6c15ea5bbae6ec30bc24a98f9375af3bc8990" 2025-03-04T20:12:16.5531370Z }, 2025-03-04T20:12:16.5531537Z { 2025-03-04T20:12:16.5531786Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5532086Z "size": 32, 2025-03-04T20:12:16.5532394Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-03-04T20:12:16.5532724Z }, 2025-03-04T20:12:16.5532894Z { 2025-03-04T20:12:16.5533244Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5533552Z "size": 159, 2025-03-04T20:12:16.5533862Z "digest": "sha256:01827e1cb87244e15ded148888c0eb9bf540965b8c29be07e853e9ba4cf3b327" 2025-03-04T20:12:16.5534195Z }, 2025-03-04T20:12:16.5534365Z { 2025-03-04T20:12:16.5534622Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5534928Z "size": 907, 2025-03-04T20:12:16.5535242Z "digest": "sha256:9118a8d55db318a9fe5af23e1d47f21e5e622c2e1047e79cb3e3b2930e597a9d" 2025-03-04T20:12:16.5535585Z }, 2025-03-04T20:12:16.5535751Z { 2025-03-04T20:12:16.5536003Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5536326Z "size": 700, 2025-03-04T20:12:16.5536637Z "digest": "sha256:8d1cf83bb67fdebcda602a033c508ed2e543bf42e81f61df50b0e481a4d9b8cd" 2025-03-04T20:12:16.5537001Z }, 2025-03-04T20:12:16.5537166Z { 2025-03-04T20:12:16.5537421Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5537722Z "size": 135, 2025-03-04T20:12:16.5538038Z "digest": "sha256:aeac28871f722d7dbda3e05aed38c4b0bcdc2a4d8e804ec84a976bc76a99aabc" 2025-03-04T20:12:16.5538402Z }, 2025-03-04T20:12:16.5538567Z { 2025-03-04T20:12:16.5538819Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5539119Z "size": 32, 2025-03-04T20:12:16.5539428Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-03-04T20:12:16.5539762Z }, 2025-03-04T20:12:16.5547280Z { 2025-03-04T20:12:16.5547760Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5548076Z "size": 157, 2025-03-04T20:12:16.5548396Z "digest": "sha256:ffdbe6e013f1afea6a7c9311dfae3ecc02983a02a121a6ff568ae70373aafbbf" 2025-03-04T20:12:16.5548735Z }, 2025-03-04T20:12:16.5548899Z { 2025-03-04T20:12:16.5549399Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5549695Z "size": 1484, 2025-03-04T20:12:16.5550005Z "digest": "sha256:0c5a33fac2aca608f4473808d0d00a68e18f732220bedee1952d2a926bdaf47a" 2025-03-04T20:12:16.5550333Z }, 2025-03-04T20:12:16.5550501Z { 2025-03-04T20:12:16.5550749Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5551042Z "size": 32, 2025-03-04T20:12:16.5551344Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-03-04T20:12:16.5551670Z }, 2025-03-04T20:12:16.5551835Z { 2025-03-04T20:12:16.5552149Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5552449Z "size": 136, 2025-03-04T20:12:16.5552748Z "digest": "sha256:a7db55a83f84a7bc071472509e2b539f38420428c02ac506ee4f3e16542e9e55" 2025-03-04T20:12:16.5553075Z }, 2025-03-04T20:12:16.5553242Z { 2025-03-04T20:12:16.5553487Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5561294Z "size": 381, 2025-03-04T20:12:16.5561630Z "digest": "sha256:73c37d17434fd27ec9e2bad8f0241ae3342cd8e22d684df9558608bc96c488ea" 2025-03-04T20:12:16.5561956Z }, 2025-03-04T20:12:16.5562126Z { 2025-03-04T20:12:16.5562390Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5562696Z "size": 32, 2025-03-04T20:12:16.5563002Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-03-04T20:12:16.5563333Z }, 2025-03-04T20:12:16.5563497Z { 2025-03-04T20:12:16.5563769Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5564061Z "size": 104, 2025-03-04T20:12:16.5564350Z "digest": "sha256:57a20cb92dc71209bf728a591bfcc5d70752bd9025bb2cf63011a05659343445" 2025-03-04T20:12:16.5564660Z }, 2025-03-04T20:12:16.5564814Z { 2025-03-04T20:12:16.5565051Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5565337Z "size": 1898, 2025-03-04T20:12:16.5565626Z "digest": "sha256:b2d315c99c689b3481609769bba07036f26cad0eef40b61f83a8011d5e835fda" 2025-03-04T20:12:16.5565928Z }, 2025-03-04T20:12:16.5566084Z { 2025-03-04T20:12:16.5566319Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5566601Z "size": 196973761, 2025-03-04T20:12:16.5566896Z "digest": "sha256:e395393695a2807512bcfccbc23881949b5ff55b5a14a898ebec2f997b99525b" 2025-03-04T20:12:16.5567199Z }, 2025-03-04T20:12:16.5567351Z { 2025-03-04T20:12:16.5567584Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5567861Z "size": 106, 2025-03-04T20:12:16.5568147Z "digest": "sha256:88bffb73f635ef4f012ce6418ab29266c4f99072c5b3f18eb955c682a0772361" 2025-03-04T20:12:16.5568456Z }, 2025-03-04T20:12:16.5568607Z { 2025-03-04T20:12:16.5568838Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5569116Z "size": 165, 2025-03-04T20:12:16.5569408Z "digest": "sha256:e51fdae441f2679f71b02dea9e5815f1c9774288f5b47f77c31fb746a4242ef8" 2025-03-04T20:12:16.5569734Z }, 2025-03-04T20:12:16.5569888Z { 2025-03-04T20:12:16.5570132Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5570429Z "size": 7943, 2025-03-04T20:12:16.5570732Z "digest": "sha256:0b5081186bc1f9d038a133cf3f4e9ea4252dc51ff4f37c0825d6486545538907" 2025-03-04T20:12:16.5571057Z }, 2025-03-04T20:12:16.5571220Z { 2025-03-04T20:12:16.5571467Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5571761Z "size": 8070, 2025-03-04T20:12:16.5572072Z "digest": "sha256:b7e9be3cf393246b723dbe14ffa9b7ec8e7d0d0ea7853f0d5e32d716f33e9f2d" 2025-03-04T20:12:16.5572407Z }, 2025-03-04T20:12:16.5572592Z { 2025-03-04T20:12:16.5572836Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5573309Z "size": 301, 2025-03-04T20:12:16.5573630Z "digest": "sha256:8a9d1164fe66bfa891980deecb5bef8d78ea8d875361fc3ffdcaf97c87dc2359" 2025-03-04T20:12:16.5574189Z }, 2025-03-04T20:12:16.5574363Z { 2025-03-04T20:12:16.5574607Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5574909Z "size": 32, 2025-03-04T20:12:16.5575217Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-03-04T20:12:16.5575557Z }, 2025-03-04T20:12:16.5575727Z { 2025-03-04T20:12:16.5575975Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5576272Z "size": 108, 2025-03-04T20:12:16.5576640Z "digest": "sha256:6802a480248fa1140e9cea64341ce0b85f49f65d4785ddbab23dd5b9f15da80b" 2025-03-04T20:12:16.5576973Z }, 2025-03-04T20:12:16.5577144Z { 2025-03-04T20:12:16.5577394Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5577695Z "size": 54145661, 2025-03-04T20:12:16.5578022Z "digest": "sha256:007eadabfae0f9c12087b7c79a6a4982efadd5b0b56ad6cdbdc1cbbac10fda94" 2025-03-04T20:12:16.5578386Z }, 2025-03-04T20:12:16.5578552Z { 2025-03-04T20:12:16.5578804Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:16.5579102Z "size": 32, 2025-03-04T20:12:16.5579400Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-03-04T20:12:16.5579797Z } 2025-03-04T20:12:16.5579963Z ] 2025-03-04T20:12:16.5580138Z } 2025-03-04T20:12:16.5580344Z + exit 0 2025-03-04T20:12:16.5615660Z ##[group]Run set -eux 2025-03-04T20:12:16.5615920Z set -eux 2025-03-04T20:12:16.5616543Z aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token | jq --raw-output '.SecretString' | jq -r .docker_hub_readonly_token | docker login --username pytorchbot --password-stdin 2025-03-04T20:12:16.5622560Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:16.5622858Z env: 2025-03-04T20:12:16.5623060Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:16.5623295Z ##[endgroup] 2025-03-04T20:12:16.5649109Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token 2025-03-04T20:12:16.5649504Z + jq --raw-output .SecretString 2025-03-04T20:12:16.5649762Z + jq -r .docker_hub_readonly_token 2025-03-04T20:12:16.5651725Z + docker login --username pytorchbot --password-stdin 2025-03-04T20:12:17.1599139Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-03-04T20:12:17.1599693Z Login Succeeded 2025-03-04T20:12:17.1599981Z Configure a credential helper to remove this warning. See 2025-03-04T20:12:17.1600465Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-03-04T20:12:17.1600739Z 2025-03-04T20:12:17.1683405Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/} 2025-03-04T20:12:17.1683711Z tag=${ECR_DOCKER_IMAGE##*/} 2025-03-04T20:12:17.1684000Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2025-03-04T20:12:17.1689673Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:17.1689999Z env: 2025-03-04T20:12:17.1690190Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:17.1690762Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:17.1691331Z ##[endgroup] 2025-03-04T20:12:17.1717063Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks-e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:17.1769234Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main 2025-03-04T20:12:17.1769616Z with: 2025-03-04T20:12:17.1770231Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:17.1770955Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:17.1771297Z env: 2025-03-04T20:12:17.1771529Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:17.1772085Z ##[endgroup] 2025-03-04T20:12:17.1797821Z ##[group]Run set -x 2025-03-04T20:12:17.1798072Z set -x 2025-03-04T20:12:17.1798269Z set +e 2025-03-04T20:12:17.1798484Z  2025-03-04T20:12:17.1798668Z login() { 2025-03-04T20:12:17.1799022Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-03-04T20:12:17.1799379Z } 2025-03-04T20:12:17.1799556Z  2025-03-04T20:12:17.1799804Z retry () { 2025-03-04T20:12:17.1800029Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-03-04T20:12:17.1800276Z } 2025-03-04T20:12:17.1800456Z  2025-03-04T20:12:17.1800649Z retry login "${DOCKER_REGISTRY}" 2025-03-04T20:12:17.1800878Z  2025-03-04T20:12:17.1801056Z set -e 2025-03-04T20:12:17.1801321Z # ignore output since only exit code is used for conditional 2025-03-04T20:12:17.1801675Z # only pull docker image if it's not available locally 2025-03-04T20:12:17.1802065Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2025-03-04T20:12:17.1802417Z  retry docker pull "${DOCKER_IMAGE}" 2025-03-04T20:12:17.1802661Z fi 2025-03-04T20:12:17.1807336Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:17.1807635Z env: 2025-03-04T20:12:17.1807837Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:17.1808409Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:17.1809040Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:17.1809342Z ##[endgroup] 2025-03-04T20:12:17.1833009Z + set +e 2025-03-04T20:12:17.1833482Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:17.1833921Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:17.1834868Z + aws ecr get-login-password --region us-east-1 2025-03-04T20:12:17.1838946Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:17.6867673Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-03-04T20:12:17.6868089Z Login Succeeded 2025-03-04T20:12:17.6868370Z Configure a credential helper to remove this warning. See 2025-03-04T20:12:17.6868845Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-03-04T20:12:17.6869115Z 2025-03-04T20:12:17.6880053Z + set -e 2025-03-04T20:12:17.6880677Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:17.7025310Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:17.7026377Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:17.9633822Z e4800fd93ba7d48bf4197a488fd32c12de647b0e: Pulling from pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks 2025-03-04T20:12:17.9634624Z 8f84a9f2102e: Pulling fs layer 2025-03-04T20:12:17.9634928Z 546bd73ecfd8: Pulling fs layer 2025-03-04T20:12:17.9635179Z 4ba5b16f22b0: Pulling fs layer 2025-03-04T20:12:17.9635841Z 64de9d43f178: Pulling fs layer 2025-03-04T20:12:17.9636097Z db0a277d2bad: Pulling fs layer 2025-03-04T20:12:17.9636336Z 70c60a789cc0: Pulling fs layer 2025-03-04T20:12:17.9636558Z 25cadc1cd3aa: Pulling fs layer 2025-03-04T20:12:17.9636791Z 374d8cd6d29f: Pulling fs layer 2025-03-04T20:12:17.9637026Z bcb2ba3f25a9: Pulling fs layer 2025-03-04T20:12:17.9637262Z fbe64186fd30: Pulling fs layer 2025-03-04T20:12:17.9637496Z 64de9d43f178: Waiting 2025-03-04T20:12:17.9637714Z a4b05775098e: Pulling fs layer 2025-03-04T20:12:17.9637943Z 8d1cf83bb67f: Pulling fs layer 2025-03-04T20:12:17.9638301Z 8dfef80cec65: Pulling fs layer 2025-03-04T20:12:17.9638529Z 18cc35ebce84: Pulling fs layer 2025-03-04T20:12:17.9638757Z 4f4fb700ef54: Pulling fs layer 2025-03-04T20:12:17.9638983Z a9ea0ac84712: Pulling fs layer 2025-03-04T20:12:17.9639207Z 0a3e73917936: Pulling fs layer 2025-03-04T20:12:17.9639430Z b8527246f7bb: Pulling fs layer 2025-03-04T20:12:17.9639650Z 7452d6d63f3b: Pulling fs layer 2025-03-04T20:12:17.9639884Z 0af9598ff0a6: Pulling fs layer 2025-03-04T20:12:17.9640110Z d3bd66660967: Pulling fs layer 2025-03-04T20:12:17.9640325Z 607b55cff127: Pulling fs layer 2025-03-04T20:12:17.9640549Z 7bba48dbd356: Pulling fs layer 2025-03-04T20:12:17.9640775Z d0ae79a6dc1b: Pulling fs layer 2025-03-04T20:12:17.9641001Z 8b2862ae18f2: Pulling fs layer 2025-03-04T20:12:17.9641222Z cd77873dfe7d: Pulling fs layer 2025-03-04T20:12:17.9641447Z cba5242bc2d3: Pulling fs layer 2025-03-04T20:12:17.9641672Z 2a41a4419d5d: Pulling fs layer 2025-03-04T20:12:17.9641895Z 2e7b43d07246: Pulling fs layer 2025-03-04T20:12:17.9642127Z 4df3af6ab4be: Pulling fs layer 2025-03-04T20:12:17.9642356Z fc852da9ad99: Pulling fs layer 2025-03-04T20:12:17.9642585Z 83e82bafb72c: Pulling fs layer 2025-03-04T20:12:17.9642809Z b94a63019124: Pulling fs layer 2025-03-04T20:12:17.9643029Z 814cabf595d9: Pulling fs layer 2025-03-04T20:12:17.9643251Z 9f1d98c20a29: Pulling fs layer 2025-03-04T20:12:17.9643468Z 567cde61f699: Pulling fs layer 2025-03-04T20:12:17.9643688Z dd11d363b70b: Pulling fs layer 2025-03-04T20:12:17.9643919Z 2befdfdc7dfc: Pulling fs layer 2025-03-04T20:12:17.9644145Z 47fc8d947ed6: Pulling fs layer 2025-03-04T20:12:17.9644363Z 615680495c63: Pulling fs layer 2025-03-04T20:12:17.9644584Z 56b65d8702b3: Pulling fs layer 2025-03-04T20:12:17.9644806Z e007be01a8bc: Pulling fs layer 2025-03-04T20:12:17.9645029Z b1c484085750: Pulling fs layer 2025-03-04T20:12:17.9645247Z 3308f34b17a2: Pulling fs layer 2025-03-04T20:12:17.9645464Z a12c1518a685: Pulling fs layer 2025-03-04T20:12:17.9645687Z 844abfd9220f: Pulling fs layer 2025-03-04T20:12:17.9645924Z 35848874efa0: Pulling fs layer 2025-03-04T20:12:17.9646143Z 3d43d95bb625: Pulling fs layer 2025-03-04T20:12:17.9646371Z 70c60a789cc0: Waiting 2025-03-04T20:12:17.9646569Z 4f4fb700ef54: Waiting 2025-03-04T20:12:17.9646765Z 2c87361802e8: Pulling fs layer 2025-03-04T20:12:17.9646980Z 4d646f8404a6: Pulling fs layer 2025-03-04T20:12:17.9647181Z a9ea0ac84712: Waiting 2025-03-04T20:12:17.9647377Z 25cadc1cd3aa: Waiting 2025-03-04T20:12:17.9647583Z fd4c3c7d17dc: Pulling fs layer 2025-03-04T20:12:17.9647801Z b6c21a5c52c9: Pulling fs layer 2025-03-04T20:12:17.9648009Z 4df3af6ab4be: Waiting 2025-03-04T20:12:17.9648211Z ce4dd732b23a: Pulling fs layer 2025-03-04T20:12:17.9648449Z 0a3e73917936: Waiting 2025-03-04T20:12:17.9648643Z 7bba48dbd356: Waiting 2025-03-04T20:12:17.9648837Z b8527246f7bb: Waiting 2025-03-04T20:12:17.9649036Z 5c7d6b4fa1e9: Pulling fs layer 2025-03-04T20:12:17.9649246Z 567cde61f699: Waiting 2025-03-04T20:12:17.9649439Z d0ae79a6dc1b: Waiting 2025-03-04T20:12:17.9649636Z fc852da9ad99: Waiting 2025-03-04T20:12:17.9649834Z 67823c882449: Pulling fs layer 2025-03-04T20:12:17.9650045Z 7452d6d63f3b: Waiting 2025-03-04T20:12:17.9650237Z 374d8cd6d29f: Waiting 2025-03-04T20:12:17.9650435Z c196c67b067c: Pulling fs layer 2025-03-04T20:12:17.9650647Z dd11d363b70b: Waiting 2025-03-04T20:12:17.9650831Z 8b2862ae18f2: Waiting 2025-03-04T20:12:17.9651019Z 56b65d8702b3: Waiting 2025-03-04T20:12:17.9651210Z 83e82bafb72c: Waiting 2025-03-04T20:12:17.9651482Z bcb2ba3f25a9: Waiting 2025-03-04T20:12:17.9651681Z 2befdfdc7dfc: Waiting 2025-03-04T20:12:17.9651873Z e007be01a8bc: Waiting 2025-03-04T20:12:17.9652073Z 3347939b4ff4: Pulling fs layer 2025-03-04T20:12:17.9652285Z cd77873dfe7d: Waiting 2025-03-04T20:12:17.9652475Z b94a63019124: Waiting 2025-03-04T20:12:17.9652664Z 9f1d98c20a29: Waiting 2025-03-04T20:12:17.9652982Z 47fc8d947ed6: Waiting 2025-03-04T20:12:17.9653214Z 01827e1cb872: Pulling fs layer 2025-03-04T20:12:17.9653436Z cba5242bc2d3: Waiting 2025-03-04T20:12:17.9653633Z 0af9598ff0a6: Waiting 2025-03-04T20:12:17.9653922Z 9118a8d55db3: Pulling fs layer 2025-03-04T20:12:17.9654133Z 2a41a4419d5d: Waiting 2025-03-04T20:12:17.9654333Z aeac28871f72: Pulling fs layer 2025-03-04T20:12:17.9654537Z 814cabf595d9: Waiting 2025-03-04T20:12:17.9654728Z 615680495c63: Waiting 2025-03-04T20:12:17.9654914Z d3bd66660967: Waiting 2025-03-04T20:12:17.9655101Z b1c484085750: Waiting 2025-03-04T20:12:17.9655299Z 2e7b43d07246: Waiting 2025-03-04T20:12:17.9655481Z 607b55cff127: Waiting 2025-03-04T20:12:17.9655680Z ffdbe6e013f1: Pulling fs layer 2025-03-04T20:12:17.9655891Z 3308f34b17a2: Waiting 2025-03-04T20:12:17.9656090Z 0c5a33fac2ac: Pulling fs layer 2025-03-04T20:12:17.9656301Z a12c1518a685: Waiting 2025-03-04T20:12:17.9656496Z a7db55a83f84: Pulling fs layer 2025-03-04T20:12:17.9656708Z 3347939b4ff4: Waiting 2025-03-04T20:12:17.9656903Z 73c37d17434f: Pulling fs layer 2025-03-04T20:12:17.9657118Z 57a20cb92dc7: Pulling fs layer 2025-03-04T20:12:17.9657327Z 8d1cf83bb67f: Waiting 2025-03-04T20:12:17.9657519Z b2d315c99c68: Pulling fs layer 2025-03-04T20:12:17.9657731Z a4b05775098e: Waiting 2025-03-04T20:12:17.9657920Z 844abfd9220f: Waiting 2025-03-04T20:12:17.9658116Z e395393695a2: Pulling fs layer 2025-03-04T20:12:17.9658334Z 88bffb73f635: Pulling fs layer 2025-03-04T20:12:17.9658543Z 8dfef80cec65: Waiting 2025-03-04T20:12:17.9658743Z e51fdae441f2: Pulling fs layer 2025-03-04T20:12:17.9658955Z 5c7d6b4fa1e9: Waiting 2025-03-04T20:12:17.9659147Z 01827e1cb872: Waiting 2025-03-04T20:12:17.9659351Z 0b5081186bc1: Pulling fs layer 2025-03-04T20:12:17.9659564Z 18cc35ebce84: Waiting 2025-03-04T20:12:17.9659756Z fd4c3c7d17dc: Waiting 2025-03-04T20:12:17.9659947Z 35848874efa0: Waiting 2025-03-04T20:12:17.9660143Z b7e9be3cf393: Pulling fs layer 2025-03-04T20:12:17.9660348Z 9118a8d55db3: Waiting 2025-03-04T20:12:17.9660539Z b6c21a5c52c9: Waiting 2025-03-04T20:12:17.9660736Z 8a9d1164fe66: Pulling fs layer 2025-03-04T20:12:17.9660944Z 2c87361802e8: Waiting 2025-03-04T20:12:17.9661128Z 3d43d95bb625: Waiting 2025-03-04T20:12:17.9661330Z 6802a480248f: Pulling fs layer 2025-03-04T20:12:17.9661535Z 4d646f8404a6: Waiting 2025-03-04T20:12:17.9661730Z 007eadabfae0: Pulling fs layer 2025-03-04T20:12:17.9662001Z c196c67b067c: Waiting 2025-03-04T20:12:17.9662203Z ce4dd732b23a: Waiting 2025-03-04T20:12:17.9662393Z b2d315c99c68: Waiting 2025-03-04T20:12:17.9662584Z ffdbe6e013f1: Waiting 2025-03-04T20:12:17.9662773Z e395393695a2: Waiting 2025-03-04T20:12:17.9662961Z aeac28871f72: Waiting 2025-03-04T20:12:17.9663160Z e51fdae441f2: Waiting 2025-03-04T20:12:17.9663342Z 6802a480248f: Waiting 2025-03-04T20:12:17.9663533Z 88bffb73f635: Waiting 2025-03-04T20:12:17.9663727Z 0b5081186bc1: Waiting 2025-03-04T20:12:17.9663919Z 007eadabfae0: Waiting 2025-03-04T20:12:17.9664113Z 0c5a33fac2ac: Waiting 2025-03-04T20:12:17.9664308Z 8a9d1164fe66: Waiting 2025-03-04T20:12:17.9664504Z 57a20cb92dc7: Waiting 2025-03-04T20:12:17.9664703Z a7db55a83f84: Waiting 2025-03-04T20:12:17.9664899Z 67823c882449: Waiting 2025-03-04T20:12:17.9665092Z fbe64186fd30: Waiting 2025-03-04T20:12:18.0341694Z 546bd73ecfd8: Verifying Checksum 2025-03-04T20:12:18.0342032Z 546bd73ecfd8: Download complete 2025-03-04T20:12:18.1099455Z 64de9d43f178: Verifying Checksum 2025-03-04T20:12:18.1111529Z 64de9d43f178: Download complete 2025-03-04T20:12:18.1901679Z db0a277d2bad: Verifying Checksum 2025-03-04T20:12:18.1903403Z db0a277d2bad: Download complete 2025-03-04T20:12:18.2813588Z 70c60a789cc0: Verifying Checksum 2025-03-04T20:12:18.2814129Z 70c60a789cc0: Download complete 2025-03-04T20:12:18.3129642Z 8f84a9f2102e: Verifying Checksum 2025-03-04T20:12:18.3130028Z 8f84a9f2102e: Download complete 2025-03-04T20:12:18.3574611Z 25cadc1cd3aa: Verifying Checksum 2025-03-04T20:12:18.3574965Z 25cadc1cd3aa: Download complete 2025-03-04T20:12:18.3929431Z 374d8cd6d29f: Verifying Checksum 2025-03-04T20:12:18.3929779Z 374d8cd6d29f: Download complete 2025-03-04T20:12:18.5697673Z fbe64186fd30: Verifying Checksum 2025-03-04T20:12:18.5698367Z fbe64186fd30: Download complete 2025-03-04T20:12:18.6350956Z a4b05775098e: Download complete 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67823c882449: Pull complete 2025-03-04T20:16:44.7466314Z c196c67b067c: Pull complete 2025-03-04T20:16:45.5205410Z 3347939b4ff4: Pull complete 2025-03-04T20:16:46.2578190Z 01827e1cb872: Pull complete 2025-03-04T20:16:46.6780272Z 9118a8d55db3: Pull complete 2025-03-04T20:16:47.4787262Z aeac28871f72: Pull complete 2025-03-04T20:16:48.4173972Z ffdbe6e013f1: Pull complete 2025-03-04T20:16:48.8309691Z 0c5a33fac2ac: Pull complete 2025-03-04T20:16:49.7171865Z a7db55a83f84: Pull complete 2025-03-04T20:16:50.1465298Z 73c37d17434f: Pull complete 2025-03-04T20:16:51.0323369Z 57a20cb92dc7: Pull complete 2025-03-04T20:16:51.4938824Z b2d315c99c68: Pull complete 2025-03-04T20:16:59.0993132Z e395393695a2: Pull complete 2025-03-04T20:16:59.5209831Z 88bffb73f635: Pull complete 2025-03-04T20:16:59.9983713Z e51fdae441f2: Pull complete 2025-03-04T20:17:00.4533151Z 0b5081186bc1: Pull complete 2025-03-04T20:17:00.8542560Z b7e9be3cf393: Pull complete 2025-03-04T20:17:01.2825140Z 8a9d1164fe66: Pull complete 2025-03-04T20:17:02.0847188Z 6802a480248f: Pull complete 2025-03-04T20:17:04.1708838Z 007eadabfae0: Pull complete 2025-03-04T20:17:04.8416468Z Digest: sha256:bd007eb2fe9e7d1c860264514799356825b802ed452803de01a654c76280cd51 2025-03-04T20:17:04.9235719Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:17:04.9600575Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:17:04.9639778Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T20:17:04.9640386Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T20:17:04.9646169Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:17:04.9646448Z env: 2025-03-04T20:17:04.9646637Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:04.9646844Z ##[endgroup] 2025-03-04T20:17:04.9722272Z Prepare all required actions 2025-03-04T20:17:04.9959422Z ##[group]Run ./.github/actions/get-workflow-job-id 2025-03-04T20:17:04.9959687Z with: 2025-03-04T20:17:04.9960066Z github-token: *** 2025-03-04T20:17:04.9960440Z env: 2025-03-04T20:17:04.9960621Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:04.9960830Z ##[endgroup] 2025-03-04T20:17:05.0279698Z ##[group]Run set -eux 2025-03-04T20:17:05.0279936Z set -eux 2025-03-04T20:17:05.0280251Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-03-04T20:17:05.0284796Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:17:05.0285067Z env: 2025-03-04T20:17:05.0285252Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:05.0285801Z GITHUB_TOKEN: *** 2025-03-04T20:17:05.0286010Z ##[endgroup] 2025-03-04T20:17:05.0307188Z + python3 .github/scripts/get_workflow_job_id.py 13661696663 i-07188c9acbdc11b95 2025-03-04T20:17:05.7027844Z setting job-id=38195235058 2025-03-04T20:17:05.7028914Z setting job-name=linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:17:05.7398146Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7 2025-03-04T20:17:05.7398739Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7 2025-03-04T20:17:05.7399177Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 & 2025-03-04T20:17:05.7399576Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2025-03-04T20:17:05.7404663Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:17:05.7405034Z env: 2025-03-04T20:17:05.7405282Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:05.7405650Z JOB_ID: 38195235058 2025-03-04T20:17:05.7406172Z JOB_NAME: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:17:05.7406611Z WORKFLOW_NAME: inductor 2025-03-04T20:17:05.7406910Z WORKFLOW_RUN_ID: 13661696663 2025-03-04T20:17:05.7407176Z ##[endgroup] 2025-03-04T20:17:06.5126752Z Defaulting to user installation because normal site-packages is not writeable 2025-03-04T20:17:06.8441978Z Collecting psutil==5.9.1 2025-03-04T20:17:06.8646599Z Downloading psutil-5.9.1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (281 kB) 2025-03-04T20:17:06.9419017Z Collecting nvidia-ml-py==11.525.84 2025-03-04T20:17:06.9507156Z Downloading nvidia_ml_py-11.525.84-py3-none-any.whl (34 kB) 2025-03-04T20:17:07.0707598Z Collecting dataclasses_json==0.6.7 2025-03-04T20:17:07.0747753Z Downloading dataclasses_json-0.6.7-py3-none-any.whl (28 kB) 2025-03-04T20:17:07.2739711Z Collecting marshmallow<4.0.0,>=3.18.0 2025-03-04T20:17:07.2779471Z Downloading marshmallow-3.26.1-py3-none-any.whl (50 kB) 2025-03-04T20:17:07.4250146Z Collecting typing-inspect<1,>=0.4.0 2025-03-04T20:17:07.4291800Z Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB) 2025-03-04T20:17:07.6005572Z Collecting packaging>=17.0 2025-03-04T20:17:07.6045069Z Downloading packaging-24.2-py3-none-any.whl (65 kB) 2025-03-04T20:17:07.7358412Z Collecting mypy-extensions>=0.3.0 2025-03-04T20:17:07.7412779Z Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB) 2025-03-04T20:17:07.8761796Z Collecting typing-extensions>=3.7.4 2025-03-04T20:17:07.8802687Z Downloading typing_extensions-4.12.2-py3-none-any.whl (37 kB) 2025-03-04T20:17:08.1979845Z Installing collected packages: typing-extensions, packaging, mypy-extensions, typing-inspect, marshmallow, psutil, nvidia-ml-py, dataclasses-json 2025-03-04T20:17:08.6817278Z Successfully installed dataclasses-json-0.6.7 marshmallow-3.26.1 mypy-extensions-1.0.0 nvidia-ml-py-11.525.84 packaging-24.2 psutil-5.9.1 typing-extensions-4.12.2 typing-inspect-0.9.0 2025-03-04T20:17:08.9572368Z Prepare all required actions 2025-03-04T20:17:08.9572771Z Getting action download info 2025-03-04T20:17:09.0877881Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2025-03-04T20:17:09.8808321Z Download action repository 'actions/download-artifact@v4' (SHA:cc203385981b70ca67e1cc392babf9cc229d5806) 2025-03-04T20:17:12.8271740Z ##[group]Run ./.github/actions/download-build-artifacts 2025-03-04T20:17:12.8273045Z with: 2025-03-04T20:17:12.8273261Z name: linux-jammy-py3.9-gcc11-build 2025-03-04T20:17:12.8273514Z s3-bucket: gha-artifacts 2025-03-04T20:17:12.8273719Z env: 2025-03-04T20:17:12.8273892Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:12.8274089Z ##[endgroup] 2025-03-04T20:17:12.8594265Z ##[group]Run seemethere/download-artifact-s3@v4 2025-03-04T20:17:12.8594528Z with: 2025-03-04T20:17:12.8594727Z name: linux-jammy-py3.9-gcc11-build 2025-03-04T20:17:12.8594967Z s3-bucket: gha-artifacts 2025-03-04T20:17:12.8595231Z region: us-east-1 2025-03-04T20:17:12.8595416Z env: 2025-03-04T20:17:12.8595596Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:12.8595796Z ##[endgroup] 2025-03-04T20:17:13.6097658Z (node:45085) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-03-04T20:17:13.6098726Z 2025-03-04T20:17:13.6102142Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-03-04T20:17:13.6102725Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-03-04T20:17:13.6103262Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-03-04T20:17:14.0489632Z Found 1 objects with prefix pytorch/pytorch/13661696663/linux-jammy-py3.9-gcc11-build/ 2025-03-04T20:17:14.0490499Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2025-03-04T20:17:18.0134369Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 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##[group]Run rm artifacts.zip 2025-03-04T20:17:23.9096019Z rm artifacts.zip 2025-03-04T20:17:23.9100934Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:17:23.9101216Z env: 2025-03-04T20:17:23.9101423Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:23.9101635Z ##[endgroup] 2025-03-04T20:17:24.0575993Z ##[group]Run df -H 2025-03-04T20:17:24.0576238Z df -H 2025-03-04T20:17:24.0580722Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:17:24.0581021Z env: 2025-03-04T20:17:24.0581205Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:24.0581411Z ##[endgroup] 2025-03-04T20:17:24.0618765Z Filesystem Size Used Avail Use% Mounted on 2025-03-04T20:17:24.0619317Z devtmpfs 4.2M 0 4.2M 0% /dev 2025-03-04T20:17:24.0619726Z tmpfs 67G 0 67G 0% /dev/shm 2025-03-04T20:17:24.0620564Z tmpfs 27G 783k 27G 1% /run 2025-03-04T20:17:24.0620886Z /dev/nvme0n1p1 215G 46G 169G 22% / 2025-03-04T20:17:24.0621192Z tmpfs 67G 13k 67G 1% /tmp 2025-03-04T20:17:24.0621589Z /dev/nvme0n1p128 11M 1.4M 9.2M 13% /boot/efi 2025-03-04T20:17:24.0649052Z Prepare all required actions 2025-03-04T20:17:24.0649384Z Getting action download info 2025-03-04T20:17:24.2184993Z ##[group]Run ./.github/actions/download-td-artifacts 2025-03-04T20:17:24.2185291Z with: 2025-03-04T20:17:24.2185485Z env: 2025-03-04T20:17:24.2185682Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:24.2185903Z ##[endgroup] 2025-03-04T20:17:24.2249457Z ##[group]Run seemethere/download-artifact-s3@v4 2025-03-04T20:17:24.2249733Z with: 2025-03-04T20:17:24.2249920Z name: td_results 2025-03-04T20:17:24.2250124Z s3-bucket: gha-artifacts 2025-03-04T20:17:24.2250342Z region: us-east-1 2025-03-04T20:17:24.2250542Z env: 2025-03-04T20:17:24.2250718Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:24.2250913Z ##[endgroup] 2025-03-04T20:17:24.6072155Z (node:45103) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-03-04T20:17:24.6074265Z 2025-03-04T20:17:24.6074780Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-03-04T20:17:24.6075192Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-03-04T20:17:24.6075584Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-03-04T20:17:24.6856349Z Found 0 objects with prefix pytorch/pytorch/13661696663/td_results/ 2025-03-04T20:17:24.6862280Z Artifact download has finished successfully 2025-03-04T20:17:24.7312972Z ##[group]Run mkdir -p .additional_ci_files 2025-03-04T20:17:24.7313274Z mkdir -p .additional_ci_files 2025-03-04T20:17:24.7313575Z mv td_results.json .additional_ci_files/td_results.json || true 2025-03-04T20:17:24.7319324Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:17:24.7319597Z env: 2025-03-04T20:17:24.7319794Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:24.7319999Z ##[endgroup] 2025-03-04T20:17:24.7365154Z mv: cannot stat 'td_results.json': No such file or directory 2025-03-04T20:17:24.7632985Z ##[group]Run .github/scripts/parse_ref.py 2025-03-04T20:17:24.7633296Z .github/scripts/parse_ref.py 2025-03-04T20:17:24.7638088Z shell: /usr/bin/bash -e {0} 2025-03-04T20:17:24.7638300Z env: 2025-03-04T20:17:24.7638489Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:24.7638693Z ##[endgroup] 2025-03-04T20:17:24.7907215Z Prepare all required actions 2025-03-04T20:17:24.7908204Z Getting action download info 2025-03-04T20:17:24.9655248Z ##[group]Run ./.github/actions/filter-test-configs 2025-03-04T20:17:24.9655557Z with: 2025-03-04T20:17:24.9655977Z github-token: *** 2025-03-04T20:17:24.9657717Z test-matrix: {"include": [{"config": "cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "inductor_torchbench_cpu_smoketest_perf", "shard": 1, "num_shards": 1, "runner": "linux.24xl.spr-metal"}]} 2025-03-04T20:17:24.9659519Z job-name: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:17:24.9659925Z env: 2025-03-04T20:17:24.9660111Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:24.9660317Z ##[endgroup] 2025-03-04T20:17:24.9968319Z ##[group]Run nick-fields/retry@v3.0.0 2025-03-04T20:17:24.9968564Z with: 2025-03-04T20:17:24.9968741Z shell: bash 2025-03-04T20:17:24.9968936Z timeout_minutes: 10 2025-03-04T20:17:24.9969128Z max_attempts: 5 2025-03-04T20:17:24.9969320Z retry_wait_seconds: 30 2025-03-04T20:17:24.9969858Z command: set -eux # PyYAML 6.0 doesn't work with MacOS x86 anymore # This must run on Python-3.7 (AmazonLinux2) so can't use request=3.32.2 python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2025-03-04T20:17:24.9970401Z polling_interval_seconds: 1 2025-03-04T20:17:24.9970637Z warning_on_retry: true 2025-03-04T20:17:24.9970842Z continue_on_error: false 2025-03-04T20:17:24.9971044Z env: 2025-03-04T20:17:24.9971233Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:24.9971706Z GITHUB_TOKEN: *** 2025-03-04T20:17:24.9971916Z ##[endgroup] 2025-03-04T20:17:25.0790721Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2025-03-04T20:17:25.2643133Z Defaulting to user installation because normal site-packages is not writeable 2025-03-04T20:17:25.4616463Z Collecting requests==2.27.1 2025-03-04T20:17:25.4852187Z Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB) 2025-03-04T20:17:25.7112350Z Collecting pyyaml==6.0.1 2025-03-04T20:17:25.7151021Z Downloading PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (738 kB) 2025-03-04T20:17:26.0570513Z Collecting charset-normalizer~=2.0.0 2025-03-04T20:17:26.0609052Z Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB) 2025-03-04T20:17:26.1939119Z Collecting certifi>=2017.4.17 2025-03-04T20:17:26.1996422Z Downloading certifi-2025.1.31-py3-none-any.whl (166 kB) 2025-03-04T20:17:26.2906147Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10) 2025-03-04T20:17:26.2914726Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10) 2025-03-04T20:17:26.3517496Z Installing collected packages: charset-normalizer, certifi, requests, pyyaml 2025-03-04T20:17:26.8495657Z Successfully installed certifi-2025.1.31 charset-normalizer-2.0.12 pyyaml-6.0.1 requests-2.27.1 2025-03-04T20:17:27.0610488Z Command completed after 1 attempt(s). 2025-03-04T20:17:27.0873376Z ##[group]Run set -x 2025-03-04T20:17:27.0873660Z set -x 2025-03-04T20:17:27.0873882Z  2025-03-04T20:17:27.0874188Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-03-04T20:17:27.0874599Z # in runner workspace 2025-03-04T20:17:27.0875201Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2025-03-04T20:17:27.0880028Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:17:27.0880308Z env: 2025-03-04T20:17:27.0880494Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:27.0880693Z ##[endgroup] 2025-03-04T20:17:27.0902176Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2025-03-04T20:17:27.1354887Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2025-03-04T20:17:27.1355224Z echo "Workflow: ${GITHUB_WORKFLOW}" 2025-03-04T20:17:27.1355474Z echo "Job name: ${JOB_NAME}" 2025-03-04T20:17:27.1355699Z  2025-03-04T20:17:27.1355978Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-03-04T20:17:27.1356303Z # in runner workspace 2025-03-04T20:17:27.1356601Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2025-03-04T20:17:27.1356952Z  --workflow "${GITHUB_WORKFLOW}" \ 2025-03-04T20:17:27.1357198Z  --job-name "${JOB_NAME}" \ 2025-03-04T20:17:27.1358811Z  --test-matrix "{"include": [{"config": "cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "inductor_torchbench_cpu_smoketest_perf", "shard": 1, "num_shards": 1, "runner": "linux.24xl.spr-metal"}]}" \ 2025-03-04T20:17:27.1360420Z  --selected-test-configs "" \ 2025-03-04T20:17:27.1360665Z  --pr-number "${PR_NUMBER}" \ 2025-03-04T20:17:27.1360902Z  --tag "${TAG}" \ 2025-03-04T20:17:27.1361130Z  --event-name "${EVENT_NAME}" \ 2025-03-04T20:17:27.1361368Z  --schedule "${SCHEDULE}" \ 2025-03-04T20:17:27.1361597Z  --branch "${HEAD_BRANCH}" 2025-03-04T20:17:27.1377337Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:17:27.1377657Z env: 2025-03-04T20:17:27.1377860Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:27.1379078Z GITHUB_TOKEN: *** 2025-03-04T20:17:27.1380093Z JOB_NAME: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:17:27.1382801Z PR_NUMBER: 2025-03-04T20:17:27.1383013Z TAG: ciflow/inductor/148205 2025-03-04T20:17:27.1383244Z EVENT_NAME: push 2025-03-04T20:17:27.1383442Z SCHEDULE: 2025-03-04T20:17:27.1383628Z HEAD_BRANCH: 2025-03-04T20:17:27.1383838Z ##[endgroup] 2025-03-04T20:17:27.1406138Z Workflow: inductor 2025-03-04T20:17:27.1406652Z Job name: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:17:27.3509107Z INFO:root:Found no test-config label on the PR, so all test configs are included 2025-03-04T20:17:27.5030758Z ##[group]Run echo "Filtered matrix:" 2025-03-04T20:17:27.5031040Z echo "Filtered matrix:" 2025-03-04T20:17:27.5032945Z echo "{"include": [{"config": "cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "inductor_torchbench_cpu_smoketest_perf", "shard": 1, "num_shards": 1, "runner": "linux.24xl.spr-metal"}]}" 2025-03-04T20:17:27.5034624Z  2025-03-04T20:17:27.5034816Z echo 2025-03-04T20:17:27.5035075Z echo "Is the current job unstable? False" 2025-03-04T20:17:27.5035311Z  2025-03-04T20:17:27.5035473Z echo 2025-03-04T20:17:27.5035673Z echo "Is keep-going label set? False" 2025-03-04T20:17:27.5035900Z  2025-03-04T20:17:27.5036059Z echo 2025-03-04T20:17:27.5036242Z echo "Renabled issues? " 2025-03-04T20:17:27.5041255Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:17:27.5041529Z env: 2025-03-04T20:17:27.5041711Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:27.5041920Z ##[endgroup] 2025-03-04T20:17:27.5066030Z Filtered matrix: 2025-03-04T20:17:27.5067877Z {include: [{config: cpu_inductor_torchbench, shard: 1, num_shards: 2, runner: linux.8xlarge.amx}, {config: cpu_inductor_torchbench, shard: 2, num_shards: 2, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_huggingface, shard: 1, num_shards: 1, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_timm, shard: 1, num_shards: 2, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_timm, shard: 2, num_shards: 2, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_torchbench, shard: 1, num_shards: 2, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_torchbench, shard: 2, num_shards: 2, runner: linux.8xlarge.amx}, {config: inductor_torchbench_cpu_smoketest_perf, shard: 1, num_shards: 1, runner: linux.24xl.spr-metal}]} 2025-03-04T20:17:27.5069337Z 2025-03-04T20:17:27.5069439Z Is the current job unstable? False 2025-03-04T20:17:27.5069586Z 2025-03-04T20:17:27.5069690Z Is keep-going label set? False 2025-03-04T20:17:27.5069826Z 2025-03-04T20:17:27.5069912Z Renabled issues? 2025-03-04T20:17:27.5350934Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-03-04T20:17:27.5351326Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-03-04T20:17:27.5355791Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:17:27.5356092Z env: 2025-03-04T20:17:27.5356273Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:27.5356470Z JOB_TIMEOUT: 240 2025-03-04T20:17:27.5356639Z ##[endgroup] 2025-03-04T20:17:27.5670174Z ##[group]Run set -x 2025-03-04T20:17:27.5670470Z set -x 2025-03-04T20:17:27.5670643Z  2025-03-04T20:17:27.5670833Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2025-03-04T20:17:27.5671113Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2025-03-04T20:17:27.5671401Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2025-03-04T20:17:27.5671739Z  TEST_COMMAND=.ci/onnx/test.sh 2025-03-04T20:17:27.5672026Z else 2025-03-04T20:17:27.5672219Z  TEST_COMMAND=.ci/pytorch/test.sh 2025-03-04T20:17:27.5672437Z fi 2025-03-04T20:17:27.5672734Z  2025-03-04T20:17:27.5672936Z # Leaving 1GB for the runner and other things 2025-03-04T20:17:27.5673371Z TOTAL_AVAILABLE_MEMORY_IN_GB=$(awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo) 2025-03-04T20:17:27.5673946Z # https://docs.docker.com/engine/containers/resource_constraints/#--memory-swap-details, the 3GB swap 2025-03-04T20:17:27.5674390Z # comes from https://github.com/pytorch/test-infra/pull/6058 2025-03-04T20:17:27.5674810Z TOTAL_MEMORY_WITH_SWAP=$(("${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}" + 3)) 2025-03-04T20:17:27.5675081Z  2025-03-04T20:17:27.5675280Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-03-04T20:17:27.5675520Z  SHM_OPTS= 2025-03-04T20:17:27.5675709Z  JENKINS_USER= 2025-03-04T20:17:27.5675956Z  # ensure that docker container cleanly exits in 12 hours 2025-03-04T20:17:27.5676260Z  # if for some reason cleanup action doesn't stop container 2025-03-04T20:17:27.5676524Z  # when job is cancelled 2025-03-04T20:17:27.5676745Z  DOCKER_SHELL_CMD="sleep 12h" 2025-03-04T20:17:27.5676955Z  2025-03-04T20:17:27.5677205Z  # since some steps are skipped on s390x, if they are necessary, run them here 2025-03-04T20:17:27.5677547Z  env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-03-04T20:17:27.5677842Z  env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-03-04T20:17:27.5678139Z else 2025-03-04T20:17:27.5678330Z  SHM_OPTS="--shm-size=${SHM_SIZE}" 2025-03-04T20:17:27.5678561Z  JENKINS_USER="--user jenkins" 2025-03-04T20:17:27.5678780Z  DOCKER_SHELL_CMD= 2025-03-04T20:17:27.5678973Z fi 2025-03-04T20:17:27.5679131Z  2025-03-04T20:17:27.5679361Z # detached container should get cleaned up by teardown_ec2_linux 2025-03-04T20:17:27.5679696Z # TODO: Stop building test binaries as part of the build phase 2025-03-04T20:17:27.5680068Z # Used for GPU_FLAG, SHM_OPTS, JENKINS_USER and DOCKER_SHELL_CMD since that doesn't play nice 2025-03-04T20:17:27.5680397Z # shellcheck disable=SC2086,SC2090 2025-03-04T20:17:27.5680625Z container_name=$(docker run \ 2025-03-04T20:17:27.5680844Z  ${GPU_FLAG:-} \ 2025-03-04T20:17:27.5681066Z  ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \ 2025-03-04T20:17:27.5681306Z  -e BUILD_ENVIRONMENT \ 2025-03-04T20:17:27.5681520Z  -e PR_NUMBER \ 2025-03-04T20:17:27.5681723Z  -e GITHUB_ACTIONS \ 2025-03-04T20:17:27.5681924Z  -e GITHUB_REPOSITORY \ 2025-03-04T20:17:27.5682135Z  -e GITHUB_WORKFLOW \ 2025-03-04T20:17:27.5682342Z  -e GITHUB_JOB \ 2025-03-04T20:17:27.5682539Z  -e GITHUB_RUN_ID \ 2025-03-04T20:17:27.5682742Z  -e GITHUB_RUN_NUMBER \ 2025-03-04T20:17:27.5682953Z  -e GITHUB_RUN_ATTEMPT \ 2025-03-04T20:17:27.5683165Z  -e JOB_ID \ 2025-03-04T20:17:27.5683353Z  -e JOB_NAME \ 2025-03-04T20:17:27.5683575Z  -e BASE_SHA \ 2025-03-04T20:17:27.5683762Z  -e BRANCH \ 2025-03-04T20:17:27.5683946Z  -e SHA1 \ 2025-03-04T20:17:27.5684132Z  -e AWS_DEFAULT_REGION \ 2025-03-04T20:17:27.5684436Z  -e IN_WHEEL_TEST \ 2025-03-04T20:17:27.5684643Z  -e SHARD_NUMBER \ 2025-03-04T20:17:27.5684842Z  -e TEST_CONFIG \ 2025-03-04T20:17:27.5685044Z  -e NUM_TEST_SHARDS \ 2025-03-04T20:17:27.5685250Z  -e REENABLED_ISSUES \ 2025-03-04T20:17:27.5685467Z  -e CONTINUE_THROUGH_ERROR \ 2025-03-04T20:17:27.5685681Z  -e VERBOSE_TEST_LOGS \ 2025-03-04T20:17:27.5685888Z  -e TEST_SHOWLOCALS \ 2025-03-04T20:17:27.5686093Z  -e NO_TEST_TIMEOUT \ 2025-03-04T20:17:27.5686291Z  -e NO_TD \ 2025-03-04T20:17:27.5686480Z  -e TD_DISTRIBUTED \ 2025-03-04T20:17:27.5686734Z  -e PR_LABELS \ 2025-03-04T20:17:27.5686950Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2025-03-04T20:17:27.5687179Z  -e SCCACHE_BUCKET \ 2025-03-04T20:17:27.5687377Z  -e SCCACHE_REGION \ 2025-03-04T20:17:27.5687567Z  -e XLA_CUDA \ 2025-03-04T20:17:27.5687774Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2025-03-04T20:17:27.5688020Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2025-03-04T20:17:27.5688498Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2025-03-04T20:17:27.5688754Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2025-03-04T20:17:27.5688990Z  -e HUGGING_FACE_HUB_TOKEN \ 2025-03-04T20:17:27.5689227Z  -e SCRIBE_GRAPHQL_ACCESS_TOKEN \ 2025-03-04T20:17:27.5689455Z  -e DASHBOARD_TAG \ 2025-03-04T20:17:27.5689662Z  -e IS_A100_RUNNER \ 2025-03-04T20:17:27.5689874Z  -e ARTIFACTS_FILE_SUFFIX \ 2025-03-04T20:17:27.5690134Z  --memory="${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}g" \ 2025-03-04T20:17:27.5690435Z  --memory-swap="${TOTAL_MEMORY_WITH_SWAP}g" \ 2025-03-04T20:17:27.5690707Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2025-03-04T20:17:27.5690975Z  --security-opt seccomp=unconfined \ 2025-03-04T20:17:27.5691212Z  --cap-add=SYS_PTRACE \ 2025-03-04T20:17:27.5691433Z  --ipc=host \ 2025-03-04T20:17:27.5691631Z  ${SHM_OPTS} \ 2025-03-04T20:17:27.5691820Z  --tty \ 2025-03-04T20:17:27.5692006Z  --detach \ 2025-03-04T20:17:27.5692206Z  --name="${container_name}" \ 2025-03-04T20:17:27.5692432Z  ${JENKINS_USER} \ 2025-03-04T20:17:27.5692683Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2025-03-04T20:17:27.5692952Z  -w /var/lib/jenkins/workspace \ 2025-03-04T20:17:27.5693176Z  "${DOCKER_IMAGE}" \ 2025-03-04T20:17:27.5693379Z  ${DOCKER_SHELL_CMD} 2025-03-04T20:17:27.5693576Z ) 2025-03-04T20:17:27.5693795Z # Propagate download.pytorch.org IP to container 2025-03-04T20:17:27.5694220Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2025-03-04T20:17:27.5694796Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2025-03-04T20:17:27.5695079Z  2025-03-04T20:17:27.5695289Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-03-04T20:17:27.5695669Z  docker exec -t "${container_name}" sh -c "python3 -m pip install -r .ci/docker/requirements-ci.txt" 2025-03-04T20:17:27.5696009Z fi 2025-03-04T20:17:27.5696192Z  2025-03-04T20:17:27.5696517Z docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2025-03-04T20:17:27.5701188Z shell: /usr/bin/bash -e {0} 2025-03-04T20:17:27.5701407Z env: 2025-03-04T20:17:27.5701591Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:27.5701843Z BUILD_ENVIRONMENT: linux-jammy-py3.9-gcc11-build 2025-03-04T20:17:27.5702088Z PR_NUMBER: 2025-03-04T20:17:27.5702282Z GITHUB_REPOSITORY: pytorch/pytorch 2025-03-04T20:17:27.5702508Z GITHUB_WORKFLOW: inductor 2025-03-04T20:17:27.5702707Z GITHUB_JOB: test 2025-03-04T20:17:27.5702890Z GITHUB_RUN_ID: 13661696663 2025-03-04T20:17:27.5703189Z GITHUB_RUN_NUMBER: 120837 2025-03-04T20:17:27.5703389Z GITHUB_RUN_ATTEMPT: 1 2025-03-04T20:17:27.5703579Z JOB_ID: 38195235058 2025-03-04T20:17:27.5703956Z JOB_NAME: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:17:27.5704323Z BRANCH: 2025-03-04T20:17:27.5704517Z SHA1: 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:17:27.5704776Z BASE_SHA: 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:17:27.5705033Z TEST_CONFIG: dynamic_cpu_inductor_torchbench 2025-03-04T20:17:27.5705264Z SHARD_NUMBER: 1 2025-03-04T20:17:27.5705442Z NUM_TEST_SHARDS: 2 2025-03-04T20:17:27.5705688Z REENABLED_ISSUES: 2025-03-04T20:17:27.5705876Z CONTINUE_THROUGH_ERROR: False 2025-03-04T20:17:27.5706086Z VERBOSE_TEST_LOGS: False 2025-03-04T20:17:27.5706277Z TEST_SHOWLOCALS: False 2025-03-04T20:17:27.5706472Z NO_TEST_TIMEOUT: False 2025-03-04T20:17:27.5706656Z NO_TD: False 2025-03-04T20:17:27.5706832Z TD_DISTRIBUTED: False 2025-03-04T20:17:27.5707061Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2025-03-04T20:17:27.5707311Z SCCACHE_REGION: us-east-1 2025-03-04T20:17:27.5707504Z SHM_SIZE: 1g 2025-03-04T20:17:27.5707970Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:17:27.5708452Z XLA_CUDA: 2025-03-04T20:17:27.5708703Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2025-03-04T20:17:27.5709003Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2025-03-04T20:17:27.5709233Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2025-03-04T20:17:27.5709456Z DASHBOARD_TAG: 2025-03-04T20:17:27.5709845Z HUGGING_FACE_HUB_TOKEN: *** 2025-03-04T20:17:27.5710130Z SCRIBE_GRAPHQL_ACCESS_TOKEN: *** 2025-03-04T20:17:27.5710341Z IS_A100_RUNNER: 0 2025-03-04T20:17:27.5710649Z ARTIFACTS_FILE_SUFFIX: test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195235058 2025-03-04T20:17:27.5710984Z ##[endgroup] 2025-03-04T20:17:27.5734736Z + [[ dynamic_cpu_inductor_torchbench == \m\u\l\t\i\g\p\u ]] 2025-03-04T20:17:27.5735228Z + [[ linux-jammy-py3.9-gcc11-build == *onnx* ]] 2025-03-04T20:17:27.5739196Z + TEST_COMMAND=.ci/pytorch/test.sh 2025-03-04T20:17:27.5744250Z ++ awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo 2025-03-04T20:17:27.5758324Z + TOTAL_AVAILABLE_MEMORY_IN_GB='122.780 ' 2025-03-04T20:17:27.5760162Z + TOTAL_MEMORY_WITH_SWAP=125 2025-03-04T20:17:27.5760580Z + [[ linux-jammy-py3.9-gcc11-build == *\s\3\9\0\x* ]] 2025-03-04T20:17:27.5765211Z + SHM_OPTS=--shm-size=1g 2025-03-04T20:17:27.5765634Z + JENKINS_USER='--user jenkins' 2025-03-04T20:17:27.5766014Z + DOCKER_SHELL_CMD= 2025-03-04T20:17:27.5766788Z +++ nproc --ignore=2 2025-03-04T20:17:27.6462827Z ++ docker run -e BUILD_ENVIRONMENT -e PR_NUMBER -e GITHUB_ACTIONS -e GITHUB_REPOSITORY -e GITHUB_WORKFLOW -e GITHUB_JOB -e GITHUB_RUN_ID -e GITHUB_RUN_NUMBER -e GITHUB_RUN_ATTEMPT -e JOB_ID -e JOB_NAME -e BASE_SHA -e BRANCH -e SHA1 -e AWS_DEFAULT_REGION -e IN_WHEEL_TEST -e SHARD_NUMBER -e TEST_CONFIG -e NUM_TEST_SHARDS -e REENABLED_ISSUES -e CONTINUE_THROUGH_ERROR -e VERBOSE_TEST_LOGS -e TEST_SHOWLOCALS -e NO_TEST_TIMEOUT -e NO_TD -e TD_DISTRIBUTED -e PR_LABELS -e MAX_JOBS=30 -e SCCACHE_BUCKET -e SCCACHE_REGION -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS -e SKIP_SCCACHE_INITIALIZATION=1 -e HUGGING_FACE_HUB_TOKEN -e SCRIBE_GRAPHQL_ACCESS_TOKEN -e DASHBOARD_TAG -e IS_A100_RUNNER -e ARTIFACTS_FILE_SUFFIX --memory=122g --memory-swap=125g --env-file=/tmp/github_env_13661696663 --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --ipc=host --shm-size=1g --tty --detach --name= --user jenkins -v /home/ec2-user/actions-runner/_work/pytorch/pytorch:/var/lib/jenkins/workspace -w /var/lib/jenkins/workspace 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:18:08.8687537Z + container_name=3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T20:18:08.8688357Z + grep download.pytorch.org /etc/hosts 2025-03-04T20:18:08.8692363Z + docker exec -i 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb sudo bash -c '/bin/cat >> /etc/hosts' 2025-03-04T20:18:08.9956861Z + echo DOCKER_CONTAINER_ID=3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T20:18:08.9957358Z + [[ linux-jammy-py3.9-gcc11-build == *\s\3\9\0\x* ]] 2025-03-04T20:18:08.9959562Z ++ echo dist/torch-2.7.0a0+git1b74980-cp39-cp39-linux_x86_64.whl 2025-03-04T20:18:08.9960877Z + docker exec -t 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb sh -c 'python3 -m pip install dist/torch-2.7.0a0+git1b74980-cp39-cp39-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh' 2025-03-04T20:18:09.3231243Z Processing ./dist/torch-2.7.0a0+git1b74980-cp39-cp39-linux_x86_64.whl (from torch==2.7.0a0+git1b74980) 2025-03-04T20:18:09.5028183Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (3.16.1) 2025-03-04T20:18:09.5029220Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (4.12.2) 2025-03-04T20:18:09.5244365Z Collecting sympy==1.13.3 (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) 2025-03-04T20:18:09.5256334Z Using cached sympy-1.13.3-py3-none-any.whl.metadata (12 kB) 2025-03-04T20:18:09.5268501Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (2.8.8) 2025-03-04T20:18:09.5274431Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (3.1.5) 2025-03-04T20:18:09.5279112Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (2024.10.0) 2025-03-04T20:18:09.5280763Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from sympy==1.13.3->torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (1.3.0) 2025-03-04T20:18:09.5296997Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (3.3.0) 2025-03-04T20:18:09.5310216Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from opt-einsum>=3.3->torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (1.22.4) 2025-03-04T20:18:09.5578716Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from jinja2->torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (3.0.2) 2025-03-04T20:18:09.5641512Z Using cached sympy-1.13.3-py3-none-any.whl (6.2 MB) 2025-03-04T20:18:10.0812160Z Installing collected packages: sympy, torch 2025-03-04T20:18:10.0823868Z Attempting uninstall: sympy 2025-03-04T20:18:10.0829429Z Found existing installation: sympy 1.13.1 2025-03-04T20:18:10.1718787Z Uninstalling sympy-1.13.1: 2025-03-04T20:18:10.6776593Z Successfully uninstalled sympy-1.13.1 2025-03-04T20:18:19.8321057Z ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. 2025-03-04T20:18:19.8322387Z timm 1.0.14 requires torchvision, which is not installed. 2025-03-04T20:18:19.8322852Z Successfully installed sympy-1.13.3 torch-2.7.0a0+git1b74980 2025-03-04T20:18:19.9181565Z + export TERM=vt100 2025-03-04T20:18:19.9181835Z + TERM=vt100 2025-03-04T20:18:19.9182034Z ++ dirname .ci/pytorch/test.sh 2025-03-04T20:18:19.9202996Z + source .ci/pytorch/common.sh 2025-03-04T20:18:19.9203448Z +++ dirname .ci/pytorch/common.sh 2025-03-04T20:18:19.9204291Z ++ source .ci/pytorch/common_utils.sh 2025-03-04T20:18:19.9204588Z +++ declare -f -t trap_add 2025-03-04T20:18:19.9204800Z ++ set -ex -o pipefail 2025-03-04T20:18:19.9205029Z ++ [[ linux-jammy-py3.9-gcc11-build == *rocm* ]] 2025-03-04T20:18:19.9205279Z ++ BUILD_TEST_LIBTORCH=0 2025-03-04T20:18:19.9205508Z + [[ linux-jammy-py3.9-gcc11-build != *rocm* ]] 2025-03-04T20:18:19.9205767Z + [[ linux-jammy-py3.9-gcc11-build != *s390x* ]] 2025-03-04T20:18:19.9206013Z + [[ -d /var/lib/jenkins/workspace ]] 2025-03-04T20:18:19.9206248Z ++ stat -c %u /var/lib/jenkins/workspace 2025-03-04T20:18:19.9213598Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2025-03-04T20:18:19.9214202Z + trap_add cleanup_workspace EXIT 2025-03-04T20:18:19.9214549Z + trap_add_cmd=cleanup_workspace 2025-03-04T20:18:19.9214777Z + shift 2025-03-04T20:18:19.9214963Z + for trap_add_name in "$@" 2025-03-04T20:18:19.9221567Z +++ trap -p EXIT 2025-03-04T20:18:19.9225288Z ++ eval 'extract_trap_cmd ' 2025-03-04T20:18:19.9225567Z +++ extract_trap_cmd 2025-03-04T20:18:19.9225788Z +++ printf '%s\n' '' 2025-03-04T20:18:19.9225997Z ++ printf '%s\n' cleanup_workspace 2025-03-04T20:18:19.9226236Z + trap -- ' 2025-03-04T20:18:19.9226417Z cleanup_workspace' EXIT 2025-03-04T20:18:19.9230587Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2025-03-04T20:18:20.2493800Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2025-03-04T20:18:20.2513712Z + echo 'Environment variables:' 2025-03-04T20:18:20.2517146Z Environment variables: 2025-03-04T20:18:20.2517543Z + env 2025-03-04T20:18:20.2517755Z INSTALLED_DB=yes 2025-03-04T20:18:20.2518038Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-03-04T20:18:20.2518666Z CONTINUE_THROUGH_ERROR=False 2025-03-04T20:18:20.2519529Z BUILD_ENVIRONMENT=linux-jammy-py3.9-gcc11-build 2025-03-04T20:18:20.2519910Z HOSTNAME=3688f39b3e9f 2025-03-04T20:18:20.2520288Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_9acb36a3-601b-4787-a218-a833361a7e18 2025-03-04T20:18:20.2520695Z GITHUB_ACTION=__self 2025-03-04T20:18:20.2520901Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-03-04T20:18:20.2521123Z GITHUB_RUN_NUMBER=120837 2025-03-04T20:18:20.2521337Z TEST_CONFIG=dynamic_cpu_inductor_torchbench 2025-03-04T20:18:20.2521572Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-03-04T20:18:20.2521802Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-03-04T20:18:20.2522025Z IS_A100_RUNNER=0 2025-03-04T20:18:20.2522455Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-03-04T20:18:20.2522683Z GITHUB_TRIGGERING_ACTOR=pytorch-bot[bot] 2025-03-04T20:18:20.2522906Z GITHUB_REF_TYPE=tag 2025-03-04T20:18:20.2523097Z TORCH_CUDA_ARCH_LIST=Maxwell 2025-03-04T20:18:20.2523329Z BASE_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:18:20.2523559Z XLA_CUDA= 2025-03-04T20:18:20.2523789Z HUGGING_FACE_HUB_TOKEN=*** 2025-03-04T20:18:20.2524071Z *** 2025-03-04T20:18:20.2524242Z GITHUB_REPOSITORY_ID=65600975 2025-03-04T20:18:20.2524443Z GITHUB_ACTIONS=true 2025-03-04T20:18:20.2524642Z SHA1=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:18:20.2524896Z GITHUB_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:18:20.2525267Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/inductor.yml@refs/tags/ciflow/inductor/148205 2025-03-04T20:18:20.2525608Z UCC_HOME=/usr 2025-03-04T20:18:20.2525784Z VERBOSE_TEST_LOGS=False 2025-03-04T20:18:20.2525989Z GITHUB_REF=refs/tags/ciflow/inductor/148205 2025-03-04T20:18:20.2526207Z SHARD_NUMBER=1 2025-03-04T20:18:20.2526385Z GITHUB_REF_PROTECTED=false 2025-03-04T20:18:20.2526579Z HOME=/var/lib/jenkins 2025-03-04T20:18:20.2526778Z GITHUB_API_URL=https://api.github.com 2025-03-04T20:18:20.2527009Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-03-04T20:18:20.2527223Z UCX_COMMIT= 2025-03-04T20:18:20.2527384Z NUM_TEST_SHARDS=2 2025-03-04T20:18:20.2527555Z UCX_HOME=/usr 2025-03-04T20:18:20.2527901Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_9acb36a3-601b-4787-a218-a833361a7e18 2025-03-04T20:18:20.2528687Z JOB_NAME=linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:18:20.2529220Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_9acb36a3-601b-4787-a218-a833361a7e18 2025-03-04T20:18:20.2529690Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-03-04T20:18:20.2530000Z GITHUB_EVENT_NAME=push 2025-03-04T20:18:20.2530185Z DASHBOARD_TAG= 2025-03-04T20:18:20.2530358Z GITHUB_RUN_ID=13661696663 2025-03-04T20:18:20.2530737Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_9acb36a3-601b-4787-a218-a833361a7e18 2025-03-04T20:18:20.2531229Z GITHUB_ACTOR=pytorch-bot[bot] 2025-03-04T20:18:20.2531428Z PR_NUMBER= 2025-03-04T20:18:20.2531587Z DESIRED_CUDA= 2025-03-04T20:18:20.2531756Z GITHUB_RUN_ATTEMPT=1 2025-03-04T20:18:20.2531944Z ANACONDA_PYTHON_VERSION=3.9 2025-03-04T20:18:20.2532176Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-03-04T20:18:20.2532411Z TERM=vt100 2025-03-04T20:18:20.2532571Z INSTALLED_VISION=yes 2025-03-04T20:18:20.2532748Z BRANCH= 2025-03-04T20:18:20.2532918Z SCCACHE_REGION=us-east-1 2025-03-04T20:18:20.2533118Z OPENSSL_ROOT_DIR=/opt/openssl 2025-03-04T20:18:20.2533319Z CUDA_PATH=/usr/local/cuda 2025-03-04T20:18:20.2533643Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-03-04T20:18:20.2533994Z GITHUB_SERVER_URL=https://github.com 2025-03-04T20:18:20.2534204Z UCC_COMMIT= 2025-03-04T20:18:20.2534372Z REENABLED_ISSUES= 2025-03-04T20:18:20.2534685Z DOCS=yes 2025-03-04T20:18:20.2534854Z SHLVL=1 2025-03-04T20:18:20.2535023Z MAX_JOBS=30 2025-03-04T20:18:20.2535205Z GITHUB_ACTOR_ID=54816060 2025-03-04T20:18:20.2535456Z GITHUB_WORKFLOW_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:18:20.2535733Z GITHUB_REF_NAME=ciflow/inductor/148205 2025-03-04T20:18:20.2536035Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-03-04T20:18:20.2536324Z GITHUB_JOB=test 2025-03-04T20:18:20.2536512Z NO_TEST_TIMEOUT=False 2025-03-04T20:18:20.2536702Z TD_DISTRIBUTED=False 2025-03-04T20:18:20.2536905Z GITHUB_REPOSITORY=pytorch/pytorch 2025-03-04T20:18:20.2537125Z GITHUB_RETENTION_DAYS=90 2025-03-04T20:18:20.2537327Z OPENSSL_DIR=/opt/openssl 2025-03-04T20:18:20.2537527Z GITHUB_ACTION_REPOSITORY= 2025-03-04T20:18:20.2538010Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-03-04T20:18:20.2538487Z GITHUB_BASE_REF= 2025-03-04T20:18:20.2538670Z INSTALLED_ACL= 2025-03-04T20:18:20.2538983Z ARTIFACTS_FILE_SUFFIX=test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195235058 2025-03-04T20:18:20.2539322Z CI=true 2025-03-04T20:18:20.2539500Z GITHUB_REPOSITORY_OWNER=pytorch 2025-03-04T20:18:20.2539705Z JOB_ID=38195235058 2025-03-04T20:18:20.2539890Z INSTALLED_PROTOBUF=yes 2025-03-04T20:18:20.2540072Z GITHUB_HEAD_REF= 2025-03-04T20:18:20.2540257Z GITHUB_ACTION_REF= 2025-03-04T20:18:20.2540474Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-03-04T20:18:20.2540727Z TEST_SHOWLOCALS=False 2025-03-04T20:18:20.2540920Z GITHUB_WORKFLOW=inductor 2025-03-04T20:18:20.2541124Z DEBIAN_FRONTEND=noninteractive 2025-03-04T20:18:20.2541516Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_9acb36a3-601b-4787-a218-a833361a7e18 2025-03-04T20:18:20.2541901Z NO_TD=False 2025-03-04T20:18:20.2542083Z SKIP_SCCACHE_INITIALIZATION=1 2025-03-04T20:18:20.2542287Z _=/usr/bin/env 2025-03-04T20:18:20.2542524Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2025-03-04T20:18:20.2740944Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch 2025-03-04T20:18:20.2741528Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/bin 2025-03-04T20:18:20.2742541Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib 2025-03-04T20:18:20.2742957Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/test 2025-03-04T20:18:20.2743540Z + BUILD_DIR=build 2025-03-04T20:18:20.2743773Z + BUILD_RENAMED_DIR=build_renamed 2025-03-04T20:18:20.2743994Z + BUILD_BIN_DIR=build/bin 2025-03-04T20:18:20.2744182Z + SHARD_NUMBER=1 2025-03-04T20:18:20.2744387Z + NUM_TEST_SHARDS=2 2025-03-04T20:18:20.2744589Z + export TORCH_SERIALIZATION_DEBUG=1 2025-03-04T20:18:20.2744814Z + TORCH_SERIALIZATION_DEBUG=1 2025-03-04T20:18:20.2745016Z + export VALGRIND=ON 2025-03-04T20:18:20.2745196Z + VALGRIND=ON 2025-03-04T20:18:20.2745404Z + [[ linux-jammy-py3.9-gcc11-build == *clang9* ]] 2025-03-04T20:18:20.2745665Z + [[ linux-jammy-py3.9-gcc11-build == *xpu* ]] 2025-03-04T20:18:20.2746026Z + [[ linux-jammy-py3.9-gcc11-build == *s390x* ]] 2025-03-04T20:18:20.2746251Z + [[ 0 == \1 ]] 2025-03-04T20:18:20.2746426Z + [[ False == \1 ]] 2025-03-04T20:18:20.2746627Z + [[ linux-jammy-py3.9-gcc11-build != *bazel* ]] 2025-03-04T20:18:20.2746871Z ++ realpath build/custom_test_artifacts 2025-03-04T20:18:20.2752428Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2025-03-04T20:18:20.2752955Z + [[ -n '' ]] 2025-03-04T20:18:20.2753267Z + echo 'Environment variables' 2025-03-04T20:18:20.2753941Z Environment variables 2025-03-04T20:18:20.2754233Z + env 2025-03-04T20:18:20.2757112Z INSTALLED_DB=yes 2025-03-04T20:18:20.2757478Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-03-04T20:18:20.2757892Z CONTINUE_THROUGH_ERROR=False 2025-03-04T20:18:20.2758174Z BUILD_ENVIRONMENT=linux-jammy-py3.9-gcc11-build 2025-03-04T20:18:20.2758434Z HOSTNAME=3688f39b3e9f 2025-03-04T20:18:20.2758805Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_9acb36a3-601b-4787-a218-a833361a7e18 2025-03-04T20:18:20.2759208Z GITHUB_ACTION=__self 2025-03-04T20:18:20.2759408Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-03-04T20:18:20.2759633Z GITHUB_RUN_NUMBER=120837 2025-03-04T20:18:20.2759851Z TEST_CONFIG=dynamic_cpu_inductor_torchbench 2025-03-04T20:18:20.2760090Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-03-04T20:18:20.2760334Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-03-04T20:18:20.2760558Z IS_A100_RUNNER=0 2025-03-04T20:18:20.2760959Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-03-04T20:18:20.2761180Z GITHUB_TRIGGERING_ACTOR=pytorch-bot[bot] 2025-03-04T20:18:20.2761396Z GITHUB_REF_TYPE=tag 2025-03-04T20:18:20.2761578Z TORCH_CUDA_ARCH_LIST=Maxwell 2025-03-04T20:18:20.2761789Z BASE_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:18:20.2762015Z XLA_CUDA= 2025-03-04T20:18:20.2762280Z HUGGING_FACE_HUB_TOKEN=*** 2025-03-04T20:18:20.2766193Z *** 2025-03-04T20:18:20.2766371Z GITHUB_REPOSITORY_ID=65600975 2025-03-04T20:18:20.2766586Z GITHUB_ACTIONS=true 2025-03-04T20:18:20.2766796Z SHA1=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:18:20.2767042Z GITHUB_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:18:20.2767413Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/inductor.yml@refs/tags/ciflow/inductor/148205 2025-03-04T20:18:20.2767750Z UCC_HOME=/usr 2025-03-04T20:18:20.2767995Z TORCH_SERIALIZATION_DEBUG=1 2025-03-04T20:18:20.2768194Z VERBOSE_TEST_LOGS=False 2025-03-04T20:18:20.2768396Z GITHUB_REF=refs/tags/ciflow/inductor/148205 2025-03-04T20:18:20.2768610Z SHARD_NUMBER=1 2025-03-04T20:18:20.2768787Z GITHUB_REF_PROTECTED=false 2025-03-04T20:18:20.2768975Z HOME=/var/lib/jenkins 2025-03-04T20:18:20.2769180Z GITHUB_API_URL=https://api.github.com 2025-03-04T20:18:20.2769409Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-03-04T20:18:20.2769612Z UCX_COMMIT= 2025-03-04T20:18:20.2769770Z NUM_TEST_SHARDS=2 2025-03-04T20:18:20.2769944Z UCX_HOME=/usr 2025-03-04T20:18:20.2770304Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_9acb36a3-601b-4787-a218-a833361a7e18 2025-03-04T20:18:20.2770852Z JOB_NAME=linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:18:20.2771383Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_9acb36a3-601b-4787-a218-a833361a7e18 2025-03-04T20:18:20.2771963Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-03-04T20:18:20.2772279Z GITHUB_EVENT_NAME=push 2025-03-04T20:18:20.2772468Z DASHBOARD_TAG= 2025-03-04T20:18:20.2772652Z GITHUB_RUN_ID=13661696663 2025-03-04T20:18:20.2773043Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_9acb36a3-601b-4787-a218-a833361a7e18 2025-03-04T20:18:20.2773449Z GITHUB_ACTOR=pytorch-bot[bot] 2025-03-04T20:18:20.2773652Z PR_NUMBER= 2025-03-04T20:18:20.2773817Z DESIRED_CUDA= 2025-03-04T20:18:20.2773992Z GITHUB_RUN_ATTEMPT=1 2025-03-04T20:18:20.2774233Z VALGRIND=ON 2025-03-04T20:18:20.2774407Z ANACONDA_PYTHON_VERSION=3.9 2025-03-04T20:18:20.2774727Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-03-04T20:18:20.2774974Z TERM=vt100 2025-03-04T20:18:20.2775143Z INSTALLED_VISION=yes 2025-03-04T20:18:20.2775317Z BRANCH= 2025-03-04T20:18:20.2776067Z SCCACHE_REGION=us-east-1 2025-03-04T20:18:20.2776339Z OPENSSL_ROOT_DIR=/opt/openssl 2025-03-04T20:18:20.2776561Z CUDA_PATH=/usr/local/cuda 2025-03-04T20:18:20.2776914Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-03-04T20:18:20.2777284Z GITHUB_SERVER_URL=https://github.com 2025-03-04T20:18:20.2777500Z UCC_COMMIT= 2025-03-04T20:18:20.2777668Z REENABLED_ISSUES= 2025-03-04T20:18:20.2777841Z DOCS=yes 2025-03-04T20:18:20.2778001Z SHLVL=1 2025-03-04T20:18:20.2778157Z MAX_JOBS=30 2025-03-04T20:18:20.2778324Z GITHUB_ACTOR_ID=54816060 2025-03-04T20:18:20.2778563Z GITHUB_WORKFLOW_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:18:20.2778834Z GITHUB_REF_NAME=ciflow/inductor/148205 2025-03-04T20:18:20.2779127Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-03-04T20:18:20.2779401Z GITHUB_JOB=test 2025-03-04T20:18:20.2779577Z NO_TEST_TIMEOUT=False 2025-03-04T20:18:20.2779762Z TD_DISTRIBUTED=False 2025-03-04T20:18:20.2779959Z GITHUB_REPOSITORY=pytorch/pytorch 2025-03-04T20:18:20.2780176Z GITHUB_RETENTION_DAYS=90 2025-03-04T20:18:20.2780368Z OPENSSL_DIR=/opt/openssl 2025-03-04T20:18:20.2780560Z GITHUB_ACTION_REPOSITORY= 2025-03-04T20:18:20.2781034Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-03-04T20:18:20.2781503Z GITHUB_BASE_REF= 2025-03-04T20:18:20.2781682Z INSTALLED_ACL= 2025-03-04T20:18:20.2781982Z ARTIFACTS_FILE_SUFFIX=test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195235058 2025-03-04T20:18:20.2782311Z CI=true 2025-03-04T20:18:20.2782482Z GITHUB_REPOSITORY_OWNER=pytorch 2025-03-04T20:18:20.2782687Z JOB_ID=38195235058 2025-03-04T20:18:20.2782916Z INSTALLED_PROTOBUF=yes 2025-03-04T20:18:20.2783098Z GITHUB_HEAD_REF= 2025-03-04T20:18:20.2783266Z GITHUB_ACTION_REF= 2025-03-04T20:18:20.2783483Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-03-04T20:18:20.2783722Z TEST_SHOWLOCALS=False 2025-03-04T20:18:20.2783908Z GITHUB_WORKFLOW=inductor 2025-03-04T20:18:20.2784107Z DEBIAN_FRONTEND=noninteractive 2025-03-04T20:18:20.2784493Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_9acb36a3-601b-4787-a218-a833361a7e18 2025-03-04T20:18:20.2784870Z NO_TD=False 2025-03-04T20:18:20.2785050Z SKIP_SCCACHE_INITIALIZATION=1 2025-03-04T20:18:20.2785249Z _=/usr/bin/env 2025-03-04T20:18:20.2785425Z + echo 'Testing pytorch' 2025-03-04T20:18:20.2785612Z Testing pytorch 2025-03-04T20:18:20.2785978Z + export LANG=C.UTF-8 2025-03-04T20:18:20.2786166Z + LANG=C.UTF-8 2025-03-04T20:18:20.2786357Z + PR_NUMBER= 2025-03-04T20:18:20.2786571Z + [[ dynamic_cpu_inductor_torchbench == \d\e\f\a\u\l\t ]] 2025-03-04T20:18:20.2786873Z + [[ dynamic_cpu_inductor_torchbench == \d\i\s\t\r\i\b\u\t\e\d ]] 2025-03-04T20:18:20.2787153Z + [[ dynamic_cpu_inductor_torchbench == \s\l\o\w ]] 2025-03-04T20:18:20.2787425Z + [[ linux-jammy-py3.9-gcc11-build == *slow-gradcheck* ]] 2025-03-04T20:18:20.2787700Z + [[ linux-jammy-py3.9-gcc11-build == *cuda* ]] 2025-03-04T20:18:20.2788019Z + [[ linux-jammy-py3.9-gcc11-build == *rocm* ]] 2025-03-04T20:18:20.2788421Z + [[ linux-jammy-py3.9-gcc11-build == *xpu* ]] 2025-03-04T20:18:20.2788679Z + [[ dynamic_cpu_inductor_torchbench == *crossref* ]] 2025-03-04T20:18:20.2788939Z + [[ linux-jammy-py3.9-gcc11-build == *rocm* ]] 2025-03-04T20:18:20.2789184Z + [[ linux-jammy-py3.9-gcc11-build == *xpu* ]] 2025-03-04T20:18:20.2789441Z + [[ linux-jammy-py3.9-gcc11-build != *-bazel-* ]] 2025-03-04T20:18:20.2789693Z + pip_install --user ninja==1.10.2 2025-03-04T20:18:20.2789965Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-03-04T20:18:20.2790395Z + python3 -m pip install --progress-bar off --user ninja==1.10.2 2025-03-04T20:18:20.6348606Z Collecting ninja==1.10.2 2025-03-04T20:18:20.6478891Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2025-03-04T20:18:20.6613545Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2025-03-04T20:18:21.1407886Z Installing collected packages: ninja 2025-03-04T20:18:21.1474878Z  WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH. 2025-03-04T20:18:21.1475594Z Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. 2025-03-04T20:18:21.1546300Z Successfully installed ninja-1.10.2 2025-03-04T20:18:21.2304838Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-03-04T20:18:21.2305789Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-03-04T20:18:21.2306344Z + [[ linux-jammy-py3.9-gcc11-build == *aarch64* ]] 2025-03-04T20:18:21.2306587Z + install_tlparse 2025-03-04T20:18:21.2306815Z + pip_install --user tlparse==0.3.30 2025-03-04T20:18:21.2307090Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-03-04T20:18:21.2307412Z + python3 -m pip install --progress-bar off --user tlparse==0.3.30 2025-03-04T20:18:21.5278900Z Collecting tlparse==0.3.30 2025-03-04T20:18:21.5356984Z Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.9 kB) 2025-03-04T20:18:21.5469578Z Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB) 2025-03-04T20:18:22.0491932Z Installing collected packages: tlparse 2025-03-04T20:18:22.0844238Z Successfully installed tlparse-0.3.30 2025-03-04T20:18:22.1586622Z ++ python -m site --user-base 2025-03-04T20:18:22.1810153Z + PATH=/var/lib/jenkins/.local/bin:/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-03-04T20:18:22.1811584Z + [[ linux-jammy-py3.9-gcc11-build == *asan* ]] 2025-03-04T20:18:22.1815719Z + [[ linux-jammy-py3.9-gcc11-build == *-debug* ]] 2025-03-04T20:18:22.1818382Z + [[ linux-jammy-py3.9-gcc11-build != *-bazel-* ]] 2025-03-04T20:18:22.1818979Z + echo 'We are not in debug mode: linux-jammy-py3.9-gcc11-build. Expect the assertion to pass' 2025-03-04T20:18:22.1821179Z We are not in debug mode: linux-jammy-py3.9-gcc11-build. Expect the assertion to pass 2025-03-04T20:18:22.1821633Z + cd test 2025-03-04T20:18:22.1827009Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2025-03-04T20:18:23.3669697Z + [[ dynamic_cpu_inductor_torchbench == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2025-03-04T20:18:23.3680752Z + [[ dynamic_cpu_inductor_torchbench == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2025-03-04T20:18:23.3681268Z + DYNAMO_BENCHMARK_FLAGS=() 2025-03-04T20:18:23.3681671Z + [[ dynamic_cpu_inductor_torchbench == *pr_time_benchmarks* ]] 2025-03-04T20:18:23.3682583Z + [[ dynamic_cpu_inductor_torchbench == *dynamo_eager* ]] 2025-03-04T20:18:23.3683339Z + [[ dynamic_cpu_inductor_torchbench == *aot_eager* ]] 2025-03-04T20:18:23.3683635Z + [[ dynamic_cpu_inductor_torchbench == *aot_inductor* ]] 2025-03-04T20:18:23.3683922Z + [[ dynamic_cpu_inductor_torchbench == *inductor* ]] 2025-03-04T20:18:23.3684196Z + [[ dynamic_cpu_inductor_torchbench != *perf* ]] 2025-03-04T20:18:23.3684481Z + DYNAMO_BENCHMARK_FLAGS+=(--inductor) 2025-03-04T20:18:23.3684743Z + [[ dynamic_cpu_inductor_torchbench == *dynamic* ]] 2025-03-04T20:18:23.3685069Z + DYNAMO_BENCHMARK_FLAGS+=(--dynamic-shapes --dynamic-batch-only) 2025-03-04T20:18:23.3685379Z + [[ dynamic_cpu_inductor_torchbench == *cpu* ]] 2025-03-04T20:18:23.3685739Z + DYNAMO_BENCHMARK_FLAGS+=(--device cpu) 2025-03-04T20:18:23.3686003Z + [[ linux-jammy-py3.9-gcc11-build == *libtorch* ]] 2025-03-04T20:18:23.3686281Z + [[ linux-jammy-py3.9-gcc11-build == *-bazel-* ]] 2025-03-04T20:18:23.3686519Z + cd test 2025-03-04T20:18:23.3686743Z + python -c 'import torch; print(torch.__config__.show())' 2025-03-04T20:18:24.3042475Z PyTorch built with: 2025-03-04T20:18:24.3044080Z - GCC 11.4 2025-03-04T20:18:24.3044386Z - C++ Version: 201703 2025-03-04T20:18:24.3049158Z - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2025-03-04T20:18:24.3053071Z - Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af) 2025-03-04T20:18:24.3057504Z - OpenMP 201511 (a.k.a. OpenMP 4.5) 2025-03-04T20:18:24.3059975Z - LAPACK is enabled (usually provided by MKL) 2025-03-04T20:18:24.3060348Z - NNPACK is enabled 2025-03-04T20:18:24.3064656Z - CPU capability usage: AVX512 2025-03-04T20:18:24.3071653Z - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=1b7498080987913ecb3aff6253c5e88f3540d911, CXX_COMPILER=/opt/cache/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=1 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Werror -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.7.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, 2025-03-04T20:18:24.3074693Z 2025-03-04T20:18:24.5082174Z + cd test 2025-03-04T20:18:24.5082733Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2025-03-04T20:18:25.4355084Z ATen/Parallel: 2025-03-04T20:18:25.4357332Z at::get_num_threads() : 16 2025-03-04T20:18:25.4357712Z at::get_num_interop_threads() : 16 2025-03-04T20:18:25.4362387Z OpenMP 201511 (a.k.a. OpenMP 4.5) 2025-03-04T20:18:25.4366904Z omp_get_max_threads() : 16 2025-03-04T20:18:25.4368814Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2025-03-04T20:18:25.4369323Z mkl_get_max_threads() : 16 2025-03-04T20:18:25.4373519Z Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af) 2025-03-04T20:18:25.4378143Z std::thread::hardware_concurrency() : 32 2025-03-04T20:18:25.4379811Z Environment variables: 2025-03-04T20:18:25.4380034Z OMP_NUM_THREADS : [not set] 2025-03-04T20:18:25.4380241Z MKL_NUM_THREADS : [not set] 2025-03-04T20:18:25.4380452Z ATen parallel backend: OpenMP 2025-03-04T20:18:25.4380599Z 2025-03-04T20:18:25.6650005Z + [[ dynamic_cpu_inductor_torchbench == *numpy_2* ]] 2025-03-04T20:18:25.6650435Z + [[ linux-jammy-py3.9-gcc11-build == *aarch64* ]] 2025-03-04T20:18:25.6651195Z + [[ dynamic_cpu_inductor_torchbench == *backward* ]] 2025-03-04T20:18:25.6654759Z + [[ dynamic_cpu_inductor_torchbench == *xla* ]] 2025-03-04T20:18:25.6675160Z + [[ dynamic_cpu_inductor_torchbench == *executorch* ]] 2025-03-04T20:18:25.6675681Z + [[ dynamic_cpu_inductor_torchbench == \j\i\t\_\l\e\g\a\c\y ]] 2025-03-04T20:18:25.6676139Z + [[ linux-jammy-py3.9-gcc11-build == *libtorch* ]] 2025-03-04T20:18:25.6676582Z + [[ dynamic_cpu_inductor_torchbench == distributed ]] 2025-03-04T20:18:25.6678851Z + [[ dynamic_cpu_inductor_torchbench == *inductor_distributed* ]] 2025-03-04T20:18:25.6684929Z + [[ dynamic_cpu_inductor_torchbench == *inductor-halide* ]] 2025-03-04T20:18:25.6692194Z + [[ dynamic_cpu_inductor_torchbench == *inductor-triton-cpu* ]] 2025-03-04T20:18:25.6692605Z + [[ dynamic_cpu_inductor_torchbench == *inductor-micro-benchmark* ]] 2025-03-04T20:18:25.6692932Z + [[ dynamic_cpu_inductor_torchbench == *huggingface* ]] 2025-03-04T20:18:25.6693246Z + [[ dynamic_cpu_inductor_torchbench == *timm* ]] 2025-03-04T20:18:25.6693521Z + [[ dynamic_cpu_inductor_torchbench == cachebench ]] 2025-03-04T20:18:25.6693798Z + [[ dynamic_cpu_inductor_torchbench == verify_cachebench ]] 2025-03-04T20:18:25.6694089Z + [[ dynamic_cpu_inductor_torchbench == *torchbench* ]] 2025-03-04T20:18:25.6694445Z + [[ dynamic_cpu_inductor_torchbench == *cpu* ]] 2025-03-04T20:18:25.6694710Z + install_torchaudio cpu 2025-03-04T20:18:25.6694915Z + local commit 2025-03-04T20:18:25.6707059Z ++ get_pinned_commit audio 2025-03-04T20:18:25.6711516Z ++ cat .github/ci_commit_pins/audio.txt 2025-03-04T20:18:25.6713648Z + commit=c670ad81fda266b6598aeeef434583eb98197ae8 2025-03-04T20:18:25.6718942Z + [[ cpu == \c\u\d\a ]] 2025-03-04T20:18:25.6721235Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8 2025-03-04T20:18:25.6721853Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-03-04T20:18:25.6722424Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8 2025-03-04T20:18:25.9294629Z Collecting git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8 2025-03-04T20:18:25.9295828Z Cloning https://github.com/pytorch/audio.git (to revision c670ad81fda266b6598aeeef434583eb98197ae8) to /tmp/pip-req-build-z_lj5hnr 2025-03-04T20:18:25.9330545Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/audio.git /tmp/pip-req-build-z_lj5hnr 2025-03-04T20:18:26.7124725Z Running command git rev-parse -q --verify 'sha^c670ad81fda266b6598aeeef434583eb98197ae8' 2025-03-04T20:18:26.7148232Z Running command git fetch -q https://github.com/pytorch/audio.git c670ad81fda266b6598aeeef434583eb98197ae8 2025-03-04T20:18:26.8015862Z Resolved https://github.com/pytorch/audio.git to commit c670ad81fda266b6598aeeef434583eb98197ae8 2025-03-04T20:18:26.8019476Z Running command git submodule update --init --recursive -q 2025-03-04T20:18:28.4998294Z Preparing metadata (setup.py) ... [?25l- done 2025-03-04T20:18:28.5032700Z [?25hRequirement already satisfied: torch in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torchaudio==2.6.0a0+c670ad8) (2.7.0a0+git1b74980) 2025-03-04T20:18:28.5055475Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (3.16.1) 2025-03-04T20:18:28.5057571Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (4.12.2) 2025-03-04T20:18:28.5058409Z Requirement already satisfied: sympy==1.13.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (1.13.3) 2025-03-04T20:18:28.5063207Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (2.8.8) 2025-03-04T20:18:28.5068727Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (3.1.5) 2025-03-04T20:18:28.5070084Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (2024.10.0) 2025-03-04T20:18:28.5079794Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from sympy==1.13.3->torch->torchaudio==2.6.0a0+c670ad8) (1.3.0) 2025-03-04T20:18:28.5462937Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from jinja2->torch->torchaudio==2.6.0a0+c670ad8) (3.0.2) 2025-03-04T20:18:28.5507034Z Building wheels for collected packages: torchaudio 2025-03-04T20:19:19.6156823Z Building wheel for torchaudio (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / done 2025-03-04T20:19:19.6184318Z [?25h Created wheel for torchaudio: filename=torchaudio-2.6.0a0+c670ad8-cp39-cp39-linux_x86_64.whl size=1820524 sha256=6da1855be2b3c6b5c0692d65f53267b3cc5458feb48617d7897f9b5c0c3fe253 2025-03-04T20:19:19.6188848Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/1c/6f/84/4c8de1f050144e10889ee8fe7b3a86ac99233001c78656be9d 2025-03-04T20:19:19.6219149Z Successfully built torchaudio 2025-03-04T20:19:20.0988785Z Installing collected packages: torchaudio 2025-03-04T20:19:20.2686105Z Successfully installed torchaudio-2.6.0a0+c670ad8 2025-03-04T20:19:20.4062414Z + install_torchvision 2025-03-04T20:19:20.4062772Z + local orig_preload 2025-03-04T20:19:20.4063016Z + local commit 2025-03-04T20:19:20.4063245Z ++ get_pinned_commit vision 2025-03-04T20:19:20.4063513Z ++ cat .github/ci_commit_pins/vision.txt 2025-03-04T20:19:20.4076061Z + commit=d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T20:19:20.4076540Z + orig_preload= 2025-03-04T20:19:20.4076847Z + '[' -n '' ']' 2025-03-04T20:19:20.4077294Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T20:19:20.4077766Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-03-04T20:19:20.4078293Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T20:19:20.7278035Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T20:19:20.7281592Z Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-0w9p_y90 2025-03-04T20:19:20.7341470Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-0w9p_y90 2025-03-04T20:19:22.0946718Z Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f' 2025-03-04T20:19:22.0980914Z Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T20:19:22.1839915Z Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T20:19:22.4735067Z Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T20:19:24.5433986Z Preparing metadata (setup.py) ... [?25l- \ done 2025-03-04T20:19:24.5477963Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torchvision==0.19.0a0+d23a6e1) (1.22.4) 2025-03-04T20:19:24.5478931Z Requirement already satisfied: torch in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.7.0a0+git1b74980) 2025-03-04T20:19:24.5479618Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torchvision==0.19.0a0+d23a6e1) (11.0.0) 2025-03-04T20:19:24.5532847Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.16.1) 2025-03-04T20:19:24.5533729Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (4.12.2) 2025-03-04T20:19:24.5534656Z Requirement already satisfied: sympy==1.13.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (1.13.3) 2025-03-04T20:19:24.5535382Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.8.8) 2025-03-04T20:19:24.5536557Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.1.5) 2025-03-04T20:19:24.5543394Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2024.10.0) 2025-03-04T20:19:24.5556134Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from sympy==1.13.3->torch->torchvision==0.19.0a0+d23a6e1) (1.3.0) 2025-03-04T20:19:24.5976879Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from jinja2->torch->torchvision==0.19.0a0+d23a6e1) (3.0.2) 2025-03-04T20:19:24.6029684Z Building wheels for collected packages: torchvision 2025-03-04T20:19:48.3870905Z Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done 2025-03-04T20:19:48.3879371Z [?25h Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp39-cp39-linux_x86_64.whl size=1218929 sha256=2eba4bfa4e05898ef84837534afabecf00eda5678f27b023a3771c7030ce0929 2025-03-04T20:19:48.3880245Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/76/25/46/00629fe1ec5f276eb28faecc4ae7c48c9ef9e6bbaa0691ad87 2025-03-04T20:19:48.3929165Z Successfully built torchvision 2025-03-04T20:19:48.9097779Z Installing collected packages: torchvision 2025-03-04T20:19:49.2416469Z Successfully installed torchvision-0.19.0a0+d23a6e1 2025-03-04T20:19:49.4390586Z + '[' -n '' ']' 2025-03-04T20:19:49.4390885Z + TORCH_CUDA_ARCH_LIST='8.0;8.6' 2025-03-04T20:19:49.4391229Z + pip_install git+https://github.com/pytorch/ao.git 2025-03-04T20:19:49.4391592Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-03-04T20:19:49.4392015Z + python3 -m pip install --progress-bar off git+https://github.com/pytorch/ao.git 2025-03-04T20:19:49.7444782Z Collecting git+https://github.com/pytorch/ao.git 2025-03-04T20:19:49.7445265Z Cloning https://github.com/pytorch/ao.git to /tmp/pip-req-build-epgvkdcj 2025-03-04T20:19:49.7488692Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/ao.git /tmp/pip-req-build-epgvkdcj 2025-03-04T20:19:50.5926303Z Resolved https://github.com/pytorch/ao.git to commit 9bcd73be6fb60cc169deeaf5b5508cb4fdaefcb5 2025-03-04T20:19:50.5927076Z Running command git submodule update --init --recursive -q 2025-03-04T20:19:54.7870698Z Preparing metadata (setup.py) ... [?25l- done 2025-03-04T20:19:54.7921171Z [?25hBuilding wheels for collected packages: torchao 2025-03-04T20:19:57.1857474Z Building wheel for torchao (setup.py) ... [?25l- \ | done 2025-03-04T20:19:57.1874510Z [?25h Created wheel for torchao: filename=torchao-0.10.0+git9bcd73be-py3-none-any.whl size=703040 sha256=d9ea522eec46609b1ca0a1461af23db1b88ca1d4880c6ddd6683c32f9dfedb73 2025-03-04T20:19:57.1875436Z Stored in directory: /tmp/pip-ephem-wheel-cache-xtordfu3/wheels/7d/5c/37/b607f0d104c0d0de07b506bc734c280ece23dce90e0c5cddc7 2025-03-04T20:19:57.1918641Z Successfully built torchao 2025-03-04T20:19:57.7091324Z Installing collected packages: torchao 2025-03-04T20:19:58.2008097Z Successfully installed torchao-0.10.0+git9bcd73be 2025-03-04T20:19:58.4585858Z + id=0 2025-03-04T20:19:58.4586201Z + pip_install opencv-python==4.8.0.74 2025-03-04T20:19:58.4586575Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-03-04T20:19:58.4586976Z + python3 -m pip install --progress-bar off opencv-python==4.8.0.74 2025-03-04T20:19:58.8647424Z Collecting opencv-python==4.8.0.74 2025-03-04T20:19:58.8793435Z Downloading opencv_python-4.8.0.74-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (19 kB) 2025-03-04T20:19:58.8885319Z Requirement already satisfied: numpy>=1.17.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from opencv-python==4.8.0.74) (1.22.4) 2025-03-04T20:19:58.8970325Z Downloading opencv_python-4.8.0.74-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (61.7 MB) 2025-03-04T20:19:59.9113792Z Installing collected packages: opencv-python 2025-03-04T20:20:00.7782454Z Successfully installed opencv-python-4.8.0.74 2025-03-04T20:20:00.9339152Z + [[ dynamic_cpu_inductor_torchbench == *inductor_torchbench_smoketest_perf* ]] 2025-03-04T20:20:00.9339662Z + [[ dynamic_cpu_inductor_torchbench == *inductor_torchbench_cpu_smoketest_perf* ]] 2025-03-04T20:20:00.9340081Z + [[ dynamic_cpu_inductor_torchbench == *torchbench_gcp_smoketest* ]] 2025-03-04T20:20:00.9340812Z + checkout_install_torchbench 2025-03-04T20:20:00.9341120Z + local commit 2025-03-04T20:20:00.9342920Z ++ 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objects: 8% (449/5606) 2025-03-04T20:20:01.1035567Z remote: Counting objects: 9% (505/5606) 2025-03-04T20:20:01.1036020Z remote: Counting objects: 10% (561/5606) 2025-03-04T20:20:01.1037457Z remote: Counting objects: 11% (617/5606) 2025-03-04T20:20:01.1037876Z remote: Counting objects: 12% (673/5606) 2025-03-04T20:20:01.1038221Z remote: Counting objects: 13% (729/5606) 2025-03-04T20:20:01.1038533Z remote: Counting objects: 14% (785/5606) 2025-03-04T20:20:01.1038914Z remote: Counting objects: 15% (841/5606) 2025-03-04T20:20:01.1039310Z remote: Counting objects: 16% (897/5606) 2025-03-04T20:20:01.1039621Z remote: Counting objects: 17% (954/5606) 2025-03-04T20:20:01.1039920Z remote: Counting objects: 18% (1010/5606) 2025-03-04T20:20:01.1040219Z remote: Counting objects: 19% (1066/5606) 2025-03-04T20:20:01.1040514Z remote: Counting objects: 20% (1122/5606) 2025-03-04T20:20:01.1040806Z remote: Counting objects: 21% (1178/5606) 2025-03-04T20:20:01.1041099Z remote: Counting objects: 22% (1234/5606) 2025-03-04T20:20:01.1041393Z remote: Counting objects: 23% (1290/5606) 2025-03-04T20:20:01.1041737Z remote: Counting objects: 24% (1346/5606) 2025-03-04T20:20:01.1042035Z remote: Counting objects: 25% (1402/5606) 2025-03-04T20:20:01.1042328Z remote: Counting objects: 26% (1458/5606) 2025-03-04T20:20:01.1042620Z remote: Counting objects: 27% (1514/5606) 2025-03-04T20:20:01.1042920Z remote: Counting objects: 28% (1570/5606) 2025-03-04T20:20:01.1043253Z remote: Counting objects: 29% (1626/5606) 2025-03-04T20:20:01.1044001Z remote: Counting objects: 30% (1682/5606) 2025-03-04T20:20:01.1044770Z remote: Counting objects: 31% (1738/5606) 2025-03-04T20:20:01.1045083Z remote: Counting objects: 32% (1794/5606) 2025-03-04T20:20:01.1045367Z remote: Counting objects: 33% (1850/5606) 2025-03-04T20:20:01.1045717Z remote: Counting objects: 34% (1907/5606) 2025-03-04T20:20:01.1046101Z remote: Counting objects: 35% (1963/5606) 2025-03-04T20:20:01.1046393Z remote: Counting objects: 36% (2019/5606) 2025-03-04T20:20:01.1046670Z remote: Counting objects: 37% (2075/5606) 2025-03-04T20:20:01.1046945Z remote: Counting objects: 38% (2131/5606) 2025-03-04T20:20:01.1047278Z remote: Counting objects: 39% (2187/5606) 2025-03-04T20:20:01.1047763Z remote: Counting objects: 40% (2243/5606) 2025-03-04T20:20:01.1048059Z remote: Counting objects: 41% (2299/5606) 2025-03-04T20:20:01.1048350Z remote: Counting objects: 42% (2355/5606) 2025-03-04T20:20:01.1048639Z remote: Counting objects: 43% (2411/5606) 2025-03-04T20:20:01.1048936Z remote: Counting objects: 44% (2467/5606) 2025-03-04T20:20:01.1049220Z remote: Counting objects: 45% (2523/5606) 2025-03-04T20:20:01.1049513Z remote: Counting objects: 46% (2579/5606) 2025-03-04T20:20:01.1049806Z remote: Counting objects: 47% (2635/5606) 2025-03-04T20:20:01.1050098Z remote: Counting objects: 48% (2691/5606) 2025-03-04T20:20:01.1050391Z remote: Counting objects: 49% (2747/5606) 2025-03-04T20:20:01.1050684Z remote: Counting objects: 50% (2803/5606) 2025-03-04T20:20:01.1050972Z remote: Counting objects: 51% (2860/5606) 2025-03-04T20:20:01.1051261Z remote: Counting objects: 52% (2916/5606) 2025-03-04T20:20:01.1051552Z remote: Counting objects: 53% (2972/5606) 2025-03-04T20:20:01.1051842Z remote: Counting objects: 54% (3028/5606) 2025-03-04T20:20:01.1052130Z remote: Counting objects: 55% (3084/5606) 2025-03-04T20:20:01.1052418Z remote: Counting objects: 56% (3140/5606) 2025-03-04T20:20:01.1052708Z remote: Counting objects: 57% (3196/5606) 2025-03-04T20:20:01.1053000Z remote: Counting objects: 58% (3252/5606) 2025-03-04T20:20:01.1053292Z remote: Counting objects: 59% (3308/5606) 2025-03-04T20:20:01.1053605Z remote: Counting objects: 60% (3364/5606) 2025-03-04T20:20:01.1054003Z remote: Counting objects: 61% (3420/5606) 2025-03-04T20:20:01.1054307Z remote: Counting objects: 62% (3476/5606) 2025-03-04T20:20:01.1054606Z remote: Counting objects: 63% (3532/5606) 2025-03-04T20:20:01.1054905Z remote: Counting objects: 64% (3588/5606) 2025-03-04T20:20:01.1055204Z remote: Counting objects: 65% (3644/5606) 2025-03-04T20:20:01.1055504Z remote: Counting objects: 66% (3700/5606) 2025-03-04T20:20:01.1055806Z remote: Counting objects: 67% (3757/5606) 2025-03-04T20:20:01.1056102Z remote: Counting objects: 68% (3813/5606) 2025-03-04T20:20:01.1056428Z remote: Counting objects: 69% (3869/5606) 2025-03-04T20:20:01.1056745Z remote: Counting objects: 70% (3925/5606) 2025-03-04T20:20:01.1057036Z remote: Counting objects: 71% (3981/5606) 2025-03-04T20:20:01.1057332Z remote: Counting objects: 72% (4037/5606) 2025-03-04T20:20:01.1057625Z remote: Counting objects: 73% (4093/5606) 2025-03-04T20:20:01.1057917Z remote: Counting objects: 74% (4149/5606) 2025-03-04T20:20:01.1058210Z remote: Counting objects: 75% (4205/5606) 2025-03-04T20:20:01.1058504Z remote: Counting objects: 76% (4261/5606) 2025-03-04T20:20:01.1058847Z remote: Counting objects: 77% (4317/5606) 2025-03-04T20:20:01.1059274Z remote: Counting objects: 78% (4373/5606) 2025-03-04T20:20:01.1060179Z remote: Counting objects: 79% (4429/5606) 2025-03-04T20:20:01.1060506Z remote: Counting objects: 80% (4485/5606) 2025-03-04T20:20:01.1060818Z remote: Counting objects: 81% (4541/5606) 2025-03-04T20:20:01.1061102Z remote: Counting objects: 82% (4597/5606) 2025-03-04T20:20:01.1061382Z remote: Counting objects: 83% (4653/5606) 2025-03-04T20:20:01.1061656Z remote: Counting objects: 84% (4710/5606) 2025-03-04T20:20:01.1062030Z remote: Counting objects: 85% (4766/5606) 2025-03-04T20:20:01.1062315Z remote: Counting objects: 86% (4822/5606) 2025-03-04T20:20:01.1062595Z remote: Counting objects: 87% (4878/5606) 2025-03-04T20:20:01.1062873Z remote: Counting objects: 88% (4934/5606) 2025-03-04T20:20:01.1063155Z remote: Counting objects: 89% (4990/5606) 2025-03-04T20:20:01.1063434Z remote: Counting objects: 90% (5046/5606) 2025-03-04T20:20:01.1063716Z remote: Counting objects: 91% (5102/5606) 2025-03-04T20:20:01.1064005Z remote: Counting objects: 92% (5158/5606) 2025-03-04T20:20:01.1064286Z remote: Counting objects: 93% (5214/5606) 2025-03-04T20:20:01.1065427Z remote: Counting objects: 94% (5270/5606) 2025-03-04T20:20:01.1065711Z remote: Counting objects: 95% (5326/5606) 2025-03-04T20:20:01.1065994Z remote: Counting objects: 96% (5382/5606) 2025-03-04T20:20:01.1066274Z remote: Counting objects: 97% (5438/5606) 2025-03-04T20:20:01.1066555Z remote: Counting objects: 98% (5494/5606) 2025-03-04T20:20:01.1066835Z remote: Counting objects: 99% (5550/5606) 2025-03-04T20:20:01.1067116Z remote: Counting objects: 100% (5606/5606) 2025-03-04T20:20:01.1067547Z remote: Counting objects: 100% (5606/5606), done. 2025-03-04T20:20:01.1067862Z remote: Compressing objects: 0% (1/677) 2025-03-04T20:20:01.1115361Z remote: Compressing objects: 1% (7/677) 2025-03-04T20:20:01.1124088Z remote: Compressing objects: 2% (14/677) 2025-03-04T20:20:01.1151307Z remote: Compressing objects: 3% (21/677) 2025-03-04T20:20:01.1181964Z remote: Compressing objects: 4% (28/677) 2025-03-04T20:20:01.1226380Z remote: Compressing objects: 5% (34/677) 2025-03-04T20:20:01.1242973Z remote: Compressing objects: 6% (41/677) 2025-03-04T20:20:01.1266907Z remote: Compressing objects: 7% (48/677) 2025-03-04T20:20:01.1273434Z remote: Compressing objects: 8% (55/677) 2025-03-04T20:20:01.1281552Z remote: Compressing objects: 9% (61/677) 2025-03-04T20:20:01.1300049Z remote: Compressing objects: 10% (68/677) 2025-03-04T20:20:01.1412586Z remote: Compressing objects: 11% (75/677) 2025-03-04T20:20:01.1489933Z remote: Compressing objects: 12% (82/677) 2025-03-04T20:20:01.1564400Z remote: Compressing objects: 13% (89/677) 2025-03-04T20:20:01.1650289Z remote: Compressing objects: 14% (95/677) 2025-03-04T20:20:01.1679476Z remote: Compressing objects: 15% (102/677) 2025-03-04T20:20:01.1735514Z remote: Compressing objects: 16% (109/677) 2025-03-04T20:20:01.1776688Z remote: Compressing objects: 17% (116/677) 2025-03-04T20:20:01.1810435Z remote: Compressing objects: 18% (122/677) 2025-03-04T20:20:01.1827933Z remote: Compressing objects: 19% (129/677) 2025-03-04T20:20:01.1866339Z remote: Compressing objects: 20% (136/677) 2025-03-04T20:20:01.1904894Z remote: Compressing objects: 21% (143/677) 2025-03-04T20:20:01.1932973Z remote: Compressing objects: 22% (149/677) 2025-03-04T20:20:01.1964173Z remote: Compressing objects: 23% (156/677) 2025-03-04T20:20:01.1976400Z remote: Compressing objects: 24% (163/677) 2025-03-04T20:20:01.2009866Z remote: Compressing objects: 25% (170/677) 2025-03-04T20:20:01.2019780Z remote: Compressing objects: 26% (177/677) 2025-03-04T20:20:01.2049307Z remote: Compressing objects: 27% (183/677) 2025-03-04T20:20:01.2059454Z remote: Compressing objects: 28% (190/677) 2025-03-04T20:20:01.2078294Z remote: Compressing objects: 29% (197/677) 2025-03-04T20:20:01.2089303Z remote: Compressing objects: 30% (204/677) 2025-03-04T20:20:01.2108174Z remote: Compressing objects: 31% (210/677) 2025-03-04T20:20:01.2122234Z remote: Compressing objects: 32% (217/677) 2025-03-04T20:20:01.2129608Z remote: Compressing objects: 33% (224/677) 2025-03-04T20:20:01.2129986Z remote: Compressing objects: 34% (231/677) 2025-03-04T20:20:01.2148241Z remote: Compressing objects: 35% (237/677) 2025-03-04T20:20:01.2154395Z remote: Compressing objects: 36% (244/677) 2025-03-04T20:20:01.2155710Z remote: Compressing objects: 37% (251/677) 2025-03-04T20:20:01.2157507Z remote: Compressing objects: 38% (258/677) 2025-03-04T20:20:01.2157996Z remote: Compressing objects: 39% (265/677) 2025-03-04T20:20:01.2158460Z remote: Compressing objects: 40% (271/677) 2025-03-04T20:20:01.2158916Z remote: Compressing objects: 41% (278/677) 2025-03-04T20:20:01.2159369Z remote: Compressing objects: 42% (285/677) 2025-03-04T20:20:01.2159691Z remote: Compressing objects: 43% (292/677) 2025-03-04T20:20:01.2160013Z remote: Compressing objects: 44% (298/677) 2025-03-04T20:20:01.2160319Z remote: Compressing objects: 45% (305/677) 2025-03-04T20:20:01.2160796Z remote: Compressing objects: 46% (312/677) 2025-03-04T20:20:01.2165531Z remote: Compressing objects: 47% (319/677) 2025-03-04T20:20:01.2174655Z remote: Compressing objects: 48% (325/677) 2025-03-04T20:20:01.2177606Z remote: Compressing objects: 49% (332/677) 2025-03-04T20:20:01.2183196Z remote: Compressing objects: 50% (339/677) 2025-03-04T20:20:01.2183748Z remote: Compressing objects: 51% (346/677) 2025-03-04T20:20:01.2189158Z remote: Compressing objects: 52% (353/677) 2025-03-04T20:20:01.2189528Z remote: Compressing objects: 53% (359/677) 2025-03-04T20:20:01.2189842Z remote: Compressing objects: 54% (366/677) 2025-03-04T20:20:01.2191114Z remote: Compressing objects: 55% (373/677) 2025-03-04T20:20:01.2191427Z remote: Compressing objects: 56% (380/677) 2025-03-04T20:20:01.2197407Z remote: Compressing objects: 57% (386/677) 2025-03-04T20:20:01.2220682Z remote: Compressing objects: 58% (393/677) 2025-03-04T20:20:01.2221036Z remote: Compressing objects: 59% (400/677) 2025-03-04T20:20:01.2221380Z remote: Compressing objects: 60% (407/677) 2025-03-04T20:20:01.2221657Z remote: Compressing objects: 61% (413/677) 2025-03-04T20:20:01.2221931Z remote: Compressing objects: 62% (420/677) 2025-03-04T20:20:01.2222218Z remote: Compressing objects: 63% (427/677) 2025-03-04T20:20:01.2222517Z remote: Compressing objects: 64% (434/677) 2025-03-04T20:20:01.2222796Z remote: Compressing objects: 65% (441/677) 2025-03-04T20:20:01.2223106Z remote: Compressing objects: 66% (447/677) 2025-03-04T20:20:01.2223373Z remote: Compressing objects: 67% (454/677) 2025-03-04T20:20:01.2223981Z remote: Compressing objects: 68% (461/677) 2025-03-04T20:20:01.2224257Z remote: Compressing objects: 69% (468/677) 2025-03-04T20:20:01.2231228Z remote: Compressing objects: 70% (474/677) 2025-03-04T20:20:01.2231724Z remote: Compressing objects: 71% (481/677) 2025-03-04T20:20:01.2232489Z remote: Compressing objects: 72% (488/677) 2025-03-04T20:20:01.2232804Z remote: Compressing objects: 73% (495/677) 2025-03-04T20:20:01.2256737Z remote: Compressing objects: 74% (501/677) 2025-03-04T20:20:01.2258680Z remote: Compressing objects: 75% (508/677) 2025-03-04T20:20:01.2259133Z remote: Compressing objects: 76% (515/677) 2025-03-04T20:20:01.2264702Z remote: Compressing objects: 77% (522/677) 2025-03-04T20:20:01.2272188Z remote: Compressing objects: 78% (529/677) 2025-03-04T20:20:01.2337925Z remote: Compressing objects: 79% (535/677) 2025-03-04T20:20:01.2376170Z remote: Compressing objects: 80% (542/677) 2025-03-04T20:20:01.2416005Z remote: Compressing objects: 81% (549/677) 2025-03-04T20:20:01.2446880Z remote: Compressing objects: 82% (556/677) 2025-03-04T20:20:01.2495705Z remote: Compressing objects: 83% (562/677) 2025-03-04T20:20:01.2535810Z remote: Compressing objects: 84% (569/677) 2025-03-04T20:20:01.2575734Z remote: Compressing objects: 85% (576/677) 2025-03-04T20:20:01.2615032Z remote: Compressing objects: 86% (583/677) 2025-03-04T20:20:01.2655857Z remote: Compressing objects: 87% (589/677) 2025-03-04T20:20:01.2695092Z remote: Compressing objects: 88% (596/677) 2025-03-04T20:20:01.2735763Z remote: Compressing objects: 89% (603/677) 2025-03-04T20:20:01.2775803Z remote: Compressing objects: 90% (610/677) 2025-03-04T20:20:01.2816748Z remote: Compressing objects: 91% (617/677) 2025-03-04T20:20:01.2855371Z remote: Compressing objects: 92% (623/677) 2025-03-04T20:20:01.2895617Z remote: Compressing objects: 93% (630/677) 2025-03-04T20:20:01.2935767Z remote: Compressing objects: 94% (637/677) 2025-03-04T20:20:01.2976053Z remote: Compressing objects: 95% (644/677) 2025-03-04T20:20:01.3010276Z remote: Compressing objects: 96% (650/677) 2025-03-04T20:20:01.3055649Z remote: Compressing objects: 97% (657/677) 2025-03-04T20:20:01.3095791Z remote: Compressing objects: 98% (664/677) 2025-03-04T20:20:01.3135875Z remote: Compressing objects: 99% (671/677) 2025-03-04T20:20:01.3175544Z remote: Compressing objects: 100% (677/677) 2025-03-04T20:20:01.3218849Z remote: Compressing objects: 100% (677/677), done. 2025-03-04T20:20:01.3255228Z Receiving objects: 0% (1/35224) 2025-03-04T20:20:01.3295124Z Receiving objects: 1% (353/35224) 2025-03-04T20:20:01.3334884Z Receiving objects: 2% (705/35224) 2025-03-04T20:20:01.3374925Z Receiving objects: 3% (1057/35224) 2025-03-04T20:20:01.3408136Z Receiving objects: 4% (1409/35224) 2025-03-04T20:20:01.3456447Z Receiving objects: 5% (1762/35224) 2025-03-04T20:20:01.3491394Z Receiving objects: 6% (2114/35224) 2025-03-04T20:20:01.3524870Z Receiving objects: 7% (2466/35224) 2025-03-04T20:20:01.3543399Z Receiving objects: 8% (2818/35224) 2025-03-04T20:20:01.3545878Z Receiving objects: 9% (3171/35224) 2025-03-04T20:20:01.3597590Z Receiving objects: 10% (3523/35224) 2025-03-04T20:20:01.3652095Z Receiving objects: 11% (3875/35224) 2025-03-04T20:20:01.3656266Z Receiving objects: 12% (4227/35224) 2025-03-04T20:20:01.3697614Z Receiving objects: 13% (4580/35224) 2025-03-04T20:20:01.3735374Z Receiving objects: 14% (4932/35224) 2025-03-04T20:20:01.3775233Z Receiving objects: 15% (5284/35224) 2025-03-04T20:20:01.3785985Z Receiving objects: 16% (5636/35224) 2025-03-04T20:20:01.3855940Z Receiving objects: 17% (5989/35224) 2025-03-04T20:20:01.3856252Z Receiving objects: 18% (6341/35224) 2025-03-04T20:20:01.3856551Z Receiving objects: 19% (6693/35224) 2025-03-04T20:20:02.2096789Z Receiving objects: 20% (7045/35224) 2025-03-04T20:20:02.2296391Z Receiving objects: 21% (7398/35224), 41.78 MiB | 83.70 MiB/s 2025-03-04T20:20:02.7050382Z Receiving objects: 21% (7428/35224), 87.14 MiB | 87.30 MiB/s 2025-03-04T20:20:02.7716693Z Receiving objects: 22% (7750/35224), 87.14 MiB | 87.30 MiB/s 2025-03-04T20:20:02.8424211Z Receiving objects: 23% (8102/35224), 127.41 MiB | 85.10 MiB/s 2025-03-04T20:20:02.9111416Z Receiving objects: 24% (8454/35224), 127.41 MiB | 85.10 MiB/s 2025-03-04T20:20:02.9803556Z Receiving objects: 25% (8806/35224), 127.41 MiB | 85.10 MiB/s 2025-03-04T20:20:03.0463975Z Receiving objects: 26% (9159/35224), 127.41 MiB | 85.10 MiB/s 2025-03-04T20:20:03.1099563Z Receiving objects: 27% (9511/35224), 127.41 MiB | 85.10 MiB/s 2025-03-04T20:20:03.2292838Z Receiving objects: 28% (9863/35224), 127.41 MiB | 85.10 MiB/s 2025-03-04T20:20:03.3092671Z Receiving objects: 28% (10141/35224), 175.09 MiB | 87.67 MiB/s 2025-03-04T20:20:03.3741559Z Receiving objects: 29% (10215/35224), 175.09 MiB | 87.67 MiB/s 2025-03-04T20:20:03.5201339Z Receiving objects: 30% (10568/35224), 175.09 MiB | 87.67 MiB/s 2025-03-04T20:20:03.7718387Z Receiving objects: 31% (10920/35224), 175.09 MiB | 87.67 MiB/s 2025-03-04T20:20:03.7899199Z Receiving objects: 32% (11272/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.7914196Z Receiving objects: 33% (11624/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.7923799Z Receiving objects: 34% (11977/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.7929512Z Receiving objects: 35% (12329/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.7935085Z Receiving objects: 36% (12681/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.7957326Z Receiving objects: 37% (13033/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.7961974Z Receiving objects: 38% (13386/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.7962596Z Receiving objects: 39% (13738/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.7984917Z Receiving objects: 40% (14090/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.7999442Z Receiving objects: 41% (14442/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.8169890Z Receiving objects: 42% (14795/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.8170243Z Receiving objects: 43% (15147/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.8173013Z Receiving objects: 44% (15499/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.8187788Z Receiving objects: 45% (15851/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.8193067Z Receiving objects: 46% (16204/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.8209274Z Receiving objects: 47% (16556/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.8224763Z Receiving objects: 48% (16908/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.8325848Z Receiving objects: 49% (17260/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.8329117Z Receiving objects: 50% (17612/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.8334034Z Receiving objects: 51% (17965/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.8553872Z Receiving objects: 52% (18317/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.8893006Z Receiving objects: 53% (18669/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.8903455Z Receiving objects: 54% (19021/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.8914886Z Receiving objects: 55% (19374/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.8927801Z Receiving objects: 56% (19726/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.8931118Z Receiving objects: 57% (20078/35224), 215.26 MiB | 86.20 MiB/s 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86.20 MiB/s 2025-03-04T20:20:03.9343045Z Receiving objects: 69% (24305/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9347758Z Receiving objects: 70% (24657/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9440706Z Receiving objects: 71% (25010/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9454077Z Receiving objects: 72% (25362/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9464007Z Receiving objects: 73% (25714/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9477011Z Receiving objects: 74% (26066/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9498794Z Receiving objects: 75% (26418/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9528935Z Receiving objects: 76% (26771/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9539330Z Receiving objects: 77% (27123/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9554580Z Receiving objects: 78% (27475/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9576488Z Receiving objects: 79% (27827/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9662606Z Receiving objects: 80% (28180/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9681808Z Receiving objects: 81% (28532/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9692933Z Receiving objects: 82% (28884/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9733490Z Receiving objects: 83% (29236/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9745101Z Receiving objects: 84% (29589/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9745701Z Receiving objects: 85% (29941/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9747880Z Receiving objects: 86% (30293/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9749739Z Receiving objects: 87% (30645/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9750242Z Receiving objects: 88% (30998/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9757362Z Receiving objects: 89% (31350/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9757892Z Receiving objects: 90% (31702/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9761119Z Receiving objects: 91% (32054/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9762941Z Receiving objects: 92% (32407/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9765937Z Receiving objects: 93% (32759/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9769934Z Receiving objects: 94% (33111/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9820763Z Receiving objects: 95% (33463/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9849581Z Receiving objects: 96% (33816/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9861613Z Receiving objects: 97% (34168/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9906944Z Receiving objects: 98% (34520/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9937556Z Receiving objects: 99% (34872/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9938403Z remote: Total 35224 (delta 5252), reused 4971 (delta 4929), pack-reused 29618 (from 2) 2025-03-04T20:20:03.9953746Z Receiving objects: 100% (35224/35224), 215.26 MiB | 86.20 MiB/s 2025-03-04T20:20:03.9958064Z Receiving objects: 100% (35224/35224), 233.51 MiB | 84.40 MiB/s, done. 2025-03-04T20:20:03.9997801Z Resolving deltas: 0% (0/19868) 2025-03-04T20:20:04.0029249Z Resolving deltas: 1% (199/19868) 2025-03-04T20:20:04.0042796Z Resolving deltas: 2% (398/19868) 2025-03-04T20:20:04.0048800Z Resolving deltas: 3% (597/19868) 2025-03-04T20:20:04.0143928Z Resolving deltas: 4% (795/19868) 2025-03-04T20:20:04.0168972Z Resolving deltas: 5% (994/19868) 2025-03-04T20:20:04.0173503Z Resolving deltas: 6% (1193/19868) 2025-03-04T20:20:04.0178054Z Resolving deltas: 7% (1391/19868) 2025-03-04T20:20:04.0185469Z Resolving deltas: 8% (1591/19868) 2025-03-04T20:20:04.0209251Z Resolving deltas: 9% (1789/19868) 2025-03-04T20:20:04.0209529Z Resolving deltas: 10% (1987/19868) 2025-03-04T20:20:04.0209743Z Resolving deltas: 11% (2186/19868) 2025-03-04T20:20:04.0209949Z Resolving deltas: 12% (2385/19868) 2025-03-04T20:20:04.0213436Z Resolving deltas: 13% (2583/19868) 2025-03-04T20:20:04.0218016Z Resolving deltas: 14% (2783/19868) 2025-03-04T20:20:04.0225158Z Resolving deltas: 15% (2981/19868) 2025-03-04T20:20:04.0229552Z Resolving deltas: 16% (3179/19868) 2025-03-04T20:20:04.0236440Z Resolving deltas: 17% (3378/19868) 2025-03-04T20:20:04.0241854Z Resolving deltas: 18% (3577/19868) 2025-03-04T20:20:04.0249737Z Resolving deltas: 19% (3775/19868) 2025-03-04T20:20:04.0259069Z Resolving deltas: 20% (3974/19868) 2025-03-04T20:20:04.0266087Z Resolving deltas: 21% (4173/19868) 2025-03-04T20:20:04.0272428Z Resolving deltas: 22% (4371/19868) 2025-03-04T20:20:04.0278510Z Resolving deltas: 23% (4570/19868) 2025-03-04T20:20:04.0293706Z Resolving deltas: 24% (4769/19868) 2025-03-04T20:20:04.0302730Z Resolving deltas: 25% (4967/19868) 2025-03-04T20:20:04.0309691Z Resolving deltas: 26% (5166/19868) 2025-03-04T20:20:04.0323516Z Resolving deltas: 27% (5366/19868) 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deltas: 43% (8544/19868) 2025-03-04T20:20:04.0441249Z Resolving deltas: 44% (8742/19868) 2025-03-04T20:20:04.0448121Z Resolving deltas: 45% (8941/19868) 2025-03-04T20:20:04.0453934Z Resolving deltas: 46% (9140/19868) 2025-03-04T20:20:04.0461454Z Resolving deltas: 47% (9338/19868) 2025-03-04T20:20:04.0468620Z Resolving deltas: 48% (9537/19868) 2025-03-04T20:20:04.0474717Z Resolving deltas: 49% (9736/19868) 2025-03-04T20:20:04.0479312Z Resolving deltas: 50% (9934/19868) 2025-03-04T20:20:04.0489819Z Resolving deltas: 51% (10133/19868) 2025-03-04T20:20:04.0493982Z Resolving deltas: 52% (10332/19868) 2025-03-04T20:20:04.0502316Z Resolving deltas: 53% (10531/19868) 2025-03-04T20:20:04.0511503Z Resolving deltas: 54% (10729/19868) 2025-03-04T20:20:04.0519761Z Resolving deltas: 55% (10928/19868) 2025-03-04T20:20:04.0527307Z Resolving deltas: 56% (11127/19868) 2025-03-04T20:20:04.0548995Z Resolving deltas: 57% (11325/19868) 2025-03-04T20:20:04.0559246Z Resolving deltas: 58% (11524/19868) 2025-03-04T20:20:04.0565678Z Resolving deltas: 59% (11728/19868) 2025-03-04T20:20:04.0573896Z Resolving deltas: 60% (11921/19868) 2025-03-04T20:20:04.0584772Z Resolving deltas: 61% (12120/19868) 2025-03-04T20:20:04.0601330Z Resolving deltas: 62% (12319/19868) 2025-03-04T20:20:04.0601657Z Resolving deltas: 63% (12518/19868) 2025-03-04T20:20:04.0609433Z Resolving deltas: 64% (12716/19868) 2025-03-04T20:20:04.0612434Z Resolving deltas: 65% (12915/19868) 2025-03-04T20:20:04.0620651Z Resolving deltas: 66% (13113/19868) 2025-03-04T20:20:04.0632202Z Resolving deltas: 67% (13312/19868) 2025-03-04T20:20:04.0641127Z Resolving deltas: 68% (13512/19868) 2025-03-04T20:20:04.0645369Z Resolving deltas: 69% (13709/19868) 2025-03-04T20:20:04.0652736Z Resolving deltas: 70% (13908/19868) 2025-03-04T20:20:04.0658782Z Resolving deltas: 71% (14107/19868) 2025-03-04T20:20:04.0671072Z Resolving deltas: 72% (14305/19868) 2025-03-04T20:20:04.0677364Z Resolving deltas: 73% (14504/19868) 2025-03-04T20:20:04.0686844Z Resolving deltas: 74% (14703/19868) 2025-03-04T20:20:04.0703382Z Resolving deltas: 75% (14901/19868) 2025-03-04T20:20:04.0707285Z Resolving deltas: 76% (15100/19868) 2025-03-04T20:20:04.0714421Z Resolving deltas: 77% (15299/19868) 2025-03-04T20:20:04.0728538Z Resolving deltas: 78% (15498/19868) 2025-03-04T20:20:04.0740558Z Resolving deltas: 79% (15696/19868) 2025-03-04T20:20:04.0747309Z Resolving deltas: 80% (15895/19868) 2025-03-04T20:20:04.0761618Z Resolving deltas: 81% (16094/19868) 2025-03-04T20:20:04.0769559Z Resolving deltas: 82% (16292/19868) 2025-03-04T20:20:04.0782602Z Resolving deltas: 83% (16491/19868) 2025-03-04T20:20:04.0792886Z Resolving deltas: 84% (16690/19868) 2025-03-04T20:20:04.0808114Z Resolving deltas: 85% (16888/19868) 2025-03-04T20:20:04.0818364Z Resolving deltas: 86% (17087/19868) 2025-03-04T20:20:04.0836299Z Resolving deltas: 87% (17286/19868) 2025-03-04T20:20:04.0843248Z Resolving deltas: 88% (17484/19868) 2025-03-04T20:20:04.0852744Z Resolving deltas: 89% (17683/19868) 2025-03-04T20:20:04.0858695Z Resolving deltas: 90% (17882/19868) 2025-03-04T20:20:04.0866329Z Resolving deltas: 91% (18080/19868) 2025-03-04T20:20:04.0885048Z Resolving deltas: 92% (18279/19868) 2025-03-04T20:20:04.0885436Z Resolving deltas: 93% (18478/19868) 2025-03-04T20:20:04.0885755Z Resolving deltas: 94% (18678/19868) 2025-03-04T20:20:04.0892782Z Resolving deltas: 95% (18875/19868) 2025-03-04T20:20:04.0893549Z Resolving deltas: 96% (19075/19868) 2025-03-04T20:20:04.0907262Z Resolving deltas: 97% (19272/19868) 2025-03-04T20:20:04.0929196Z Resolving deltas: 98% (19471/19868) 2025-03-04T20:20:04.0957594Z Resolving deltas: 99% (19670/19868) 2025-03-04T20:20:04.0957978Z Resolving deltas: 100% (19868/19868) 2025-03-04T20:20:04.0958228Z Resolving deltas: 100% (19868/19868), done. 2025-03-04T20:20:05.0443923Z + pushd torchbench 2025-03-04T20:20:05.0448665Z ~/workspace/torchbench ~/workspace 2025-03-04T20:20:05.0449003Z + git checkout 373ffb19dc470f4423a3176a4133f8f4b3cdb5bd 2025-03-04T20:20:05.0666219Z Note: switching to '373ffb19dc470f4423a3176a4133f8f4b3cdb5bd'. 2025-03-04T20:20:05.0666482Z 2025-03-04T20:20:05.0666999Z You are in 'detached HEAD' state. You can look around, make experimental 2025-03-04T20:20:05.0667365Z changes and commit them, and you can discard any commits you make in this 2025-03-04T20:20:05.0667731Z state without impacting any branches by switching back to a branch. 2025-03-04T20:20:05.0667939Z 2025-03-04T20:20:05.0668096Z If you want to create a new branch to retain commits you create, you may 2025-03-04T20:20:05.0668434Z do so (now or later) by using -c with the switch command. Example: 2025-03-04T20:20:05.0668626Z 2025-03-04T20:20:05.0668740Z git switch -c 2025-03-04T20:20:05.0668949Z 2025-03-04T20:20:05.0669064Z Or undo this operation with: 2025-03-04T20:20:05.0669257Z 2025-03-04T20:20:05.0669352Z git switch - 2025-03-04T20:20:05.0669477Z 2025-03-04T20:20:05.0669678Z Turn off this advice by setting config variable advice.detachedHead to false 2025-03-04T20:20:05.0669900Z 2025-03-04T20:20:05.0670047Z HEAD is now at 373ffb19 Copy model before benchmark warmup runs (#145858) 2025-03-04T20:20:05.0670374Z + '[' '' ']' 2025-03-04T20:20:05.0671215Z + python install.py --continue_on_fail 2025-03-04T20:20:09.7882348Z checking packages numpy, torch, torchvision, torchaudio are installed, generating constaints...OK 2025-03-04T20:20:34.2522253Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/BERT_pytorch...OK 2025-03-04T20:20:47.0065209Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/Background_Matting...OK 2025-03-04T20:20:58.7361948Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/LearningToPaint...OK 2025-03-04T20:21:10.3917838Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/Super_SloMo...OK 2025-03-04T20:21:20.4035059Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/alexnet...OK 2025-03-04T20:21:35.5743895Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_edgecnn...OK 2025-03-04T20:21:47.0245977Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_gcn...OK 2025-03-04T20:21:58.4864551Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_gin...OK 2025-03-04T20:22:09.9086539Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_sage...OK 2025-03-04T20:22:09.9088935Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/cm3leon_generate...SKIP - No install.py is found 2025-03-04T20:22:20.0321520Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/dcgan...OK 2025-03-04T20:22:31.4722211Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/demucs...OK 2025-03-04T20:22:40.9647928Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/densenet121...OK 2025-03-04T20:23:20.0745809Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_101_c4...OK 2025-03-04T20:23:39.6561269Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_101_dc5...OK 2025-03-04T20:23:57.3063371Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_101_fpn...OK 2025-03-04T20:24:15.2586753Z running setup for 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2025-03-04T20:28:27.5833863Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/functorch_maml_omniglot...OK 2025-03-04T20:28:44.3450374Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Albert...OK 2025-03-04T20:29:01.4462613Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Bart...OK 2025-03-04T20:29:17.3197812Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Bert...OK 2025-03-04T20:29:34.9582367Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Bert_large...OK 2025-03-04T20:29:51.2320296Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_BigBird...OK 2025-03-04T20:30:06.3015948Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_DistilBert...OK 2025-03-04T20:30:22.1674845Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_GPT2...OK 2025-03-04T20:30:46.1015451Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_GPT2_large...OK 2025-03-04T20:31:02.6965216Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Longformer...OK 2025-03-04T20:31:17.4279103Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Reformer...OK 2025-03-04T20:31:37.3070608Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Roberta_base...OK 2025-03-04T20:31:53.2904505Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5...OK 2025-03-04T20:32:10.2970201Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5_base...OK 2025-03-04T20:32:10.2970875Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5_generate...SKIP - No install.py is found 2025-03-04T20:32:31.9777704Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5_large...OK 2025-03-04T20:32:45.8920220Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Whisper...OK 2025-03-04T20:32:45.8921539Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_clip...SKIP - No install.py is found 2025-03-04T20:33:04.3292233Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_distil_whisper...OK 2025-03-04T20:33:17.3458743Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/lennard_jones...OK 2025-03-04T20:33:29.1663588Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/llama...OK 2025-03-04T20:34:10.5957127Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/llama_v2_7b_16h...OK 2025-03-04T20:35:19.5331807Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/llava...OK 2025-03-04T20:35:30.3713797Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/maml...OK 2025-03-04T20:35:42.6161681Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/maml_omniglot...OK 2025-03-04T20:35:42.6162500Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/microbench_unbacked_tolist_sum...SKIP - No install.py is found 2025-03-04T20:35:53.5577593Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mnasnet1_0...OK 2025-03-04T20:36:04.6870541Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mobilenet_v2...OK 2025-03-04T20:36:14.6079769Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mobilenet_v2_quantized_qat...OK 2025-03-04T20:36:25.1071071Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mobilenet_v3_large...OK 2025-03-04T20:36:35.4117182Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/moco...OK 2025-03-04T20:37:01.1435234Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/moondream...OK 2025-03-04T20:37:01.1438421Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/nanogpt...SKIP - No install.py is found 2025-03-04T20:37:12.9936494Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/nvidia_deeprecommender...OK 2025-03-04T20:37:25.9666091Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/opacus_cifar10...OK 2025-03-04T20:37:36.0949147Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/phlippe_densenet...OK 2025-03-04T20:37:46.2211172Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/phlippe_resnet...OK 2025-03-04T20:37:57.1794038Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pyhpc_equation_of_state...OK 2025-03-04T20:38:08.9085567Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pyhpc_isoneutral_mixing...OK 2025-03-04T20:38:19.6299236Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pyhpc_turbulent_kinetic_energy...OK 2025-03-04T20:38:36.4853118Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pytorch_CycleGAN_and_pix2pix...OK 2025-03-04T20:38:47.9888901Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pytorch_stargan...OK 2025-03-04T20:39:01.5617969Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pytorch_unet...OK 2025-03-04T20:39:11.3584387Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet152...OK 2025-03-04T20:39:22.0143808Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet18...OK 2025-03-04T20:39:32.5438166Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet50...OK 2025-03-04T20:39:42.5231336Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet50_quantized_qat...OK 2025-03-04T20:39:52.6923105Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnext50_32x4d...OK 2025-03-04T20:40:13.0678644Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/sam...OK 2025-03-04T20:40:33.3719543Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/sam_fast...OK 2025-03-04T20:40:44.2290701Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/shufflenet_v2_x1_0...OK 2025-03-04T20:40:44.2291428Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/simple_gpt...SKIP - No install.py is found 2025-03-04T20:40:44.2292168Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/simple_gpt_tp_manual...SKIP - No install.py is found 2025-03-04T20:40:57.1952944Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/soft_actor_critic...OK 2025-03-04T20:41:09.8131083Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/speech_transformer...OK 2025-03-04T20:41:20.8902003Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/squeezenet1_1...OK 2025-03-04T20:41:55.2785094Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/stable_diffusion_text_encoder...OK 2025-03-04T20:42:11.1620304Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/stable_diffusion_unet...OK 2025-03-04T20:42:28.1365916Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/tacotron2...OK 2025-03-04T20:42:46.4010288Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_efficientdet...OK 2025-03-04T20:42:57.6166059Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_efficientnet...OK 2025-03-04T20:43:08.7635854Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_nfnet...OK 2025-03-04T20:43:20.4045820Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_regnet...OK 2025-03-04T20:43:32.4911456Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_resnest...OK 2025-03-04T20:43:43.4370738Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_vision_transformer...OK 2025-03-04T20:43:54.6063312Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_vision_transformer_large...OK 2025-03-04T20:44:06.1087406Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_vovnet...OK 2025-03-04T20:44:23.4094516Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/torch_multimodal_clip...OK 2025-03-04T20:44:38.6204745Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/tts_angular...OK 2025-03-04T20:44:48.9511681Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/vgg16...OK 2025-03-04T20:45:00.9554820Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/vision_maskrcnn...OK 2025-03-04T20:45:13.1928889Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/yolov3...OK 2025-03-04T20:45:18.5526714Z installed torchbench with package constraints: {'numpy': '1.22.4', 'torch': '2.7.0a0+git1b74980', 'torchvision': '0.19.0a0+d23a6e1', 'torchaudio': '2.6.0a0+c670ad8'} 2025-03-04T20:45:18.8241123Z + pip install transformers==4.38.1 2025-03-04T20:45:19.1941696Z Collecting transformers==4.38.1 2025-03-04T20:45:19.2076224Z Downloading transformers-4.38.1-py3-none-any.whl.metadata (131 kB) 2025-03-04T20:45:19.3567933Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (3.16.1) 2025-03-04T20:45:19.3570667Z Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (0.29.1) 2025-03-04T20:45:19.3573015Z Requirement already satisfied: numpy>=1.17 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (1.22.4) 2025-03-04T20:45:19.3573625Z Requirement already satisfied: packaging>=20.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (24.2) 2025-03-04T20:45:19.3574697Z Requirement already satisfied: pyyaml>=5.1 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (6.0.2) 2025-03-04T20:45:19.3577058Z Requirement already satisfied: regex!=2019.12.17 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (2024.11.6) 2025-03-04T20:45:19.3577745Z Requirement already satisfied: requests in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (2.32.3) 2025-03-04T20:45:19.4536861Z Collecting tokenizers<0.19,>=0.14 (from transformers==4.38.1) 2025-03-04T20:45:19.4549187Z Using cached tokenizers-0.15.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB) 2025-03-04T20:45:19.4576099Z Requirement already satisfied: safetensors>=0.4.1 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (0.5.3) 2025-03-04T20:45:19.4576990Z Requirement already satisfied: tqdm>=4.27 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (4.67.1) 2025-03-04T20:45:19.4741840Z Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.38.1) (2024.10.0) 2025-03-04T20:45:19.4742759Z Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.38.1) (4.12.2) 2025-03-04T20:45:19.4881625Z Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from requests->transformers==4.38.1) (3.4.1) 2025-03-04T20:45:19.4888577Z Requirement already satisfied: idna<4,>=2.5 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from requests->transformers==4.38.1) (3.10) 2025-03-04T20:45:19.4889647Z Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from requests->transformers==4.38.1) (1.26.20) 2025-03-04T20:45:19.4891260Z Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from requests->transformers==4.38.1) (2025.1.31) 2025-03-04T20:45:19.5304549Z Downloading transformers-4.38.1-py3-none-any.whl (8.5 MB) 2025-03-04T20:45:19.5986085Z [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/8.5 MB ? eta -:--:-- 2025-03-04T20:45:19.5986775Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 8.5/8.5 MB 124.1 MB/s eta 0:00:00 2025-03-04T20:45:19.5987450Z [?25hUsing cached tokenizers-0.15.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB) 2025-03-04T20:45:20.3833571Z Installing collected packages: tokenizers, transformers 2025-03-04T20:45:20.3833928Z Attempting uninstall: tokenizers 2025-03-04T20:45:20.3847037Z Found existing installation: tokenizers 0.19.1 2025-03-04T20:45:20.3876916Z Uninstalling tokenizers-0.19.1: 2025-03-04T20:45:20.3885930Z Successfully uninstalled tokenizers-0.19.1 2025-03-04T20:45:20.4513799Z Attempting uninstall: transformers 2025-03-04T20:45:20.4531443Z Found existing installation: transformers 4.44.2 2025-03-04T20:45:20.5643656Z Uninstalling transformers-4.44.2: 2025-03-04T20:45:20.5832802Z Successfully uninstalled transformers-4.44.2 2025-03-04T20:45:23.8492954Z Successfully installed tokenizers-0.15.2 transformers-4.38.1 2025-03-04T20:45:23.9904936Z + echo 'Print all dependencies after TorchBench is installed' 2025-03-04T20:45:23.9905363Z Print all dependencies after TorchBench is installed 2025-03-04T20:45:23.9909037Z + python -mpip freeze 2025-03-04T20:45:24.3227076Z absl-py==2.1.0 2025-03-04T20:45:24.3227358Z accelerate==1.4.0 2025-03-04T20:45:24.3228631Z aiohappyeyeballs==2.4.6 2025-03-04T20:45:24.3228961Z aiohttp==3.11.13 2025-03-04T20:45:24.3229213Z aiosignal==1.3.2 2025-03-04T20:45:24.3229438Z alabaster==0.7.16 2025-03-04T20:45:24.3229625Z annotated-types==0.7.0 2025-03-04T20:45:24.3229842Z antlr4-python3-runtime==4.9.3 2025-03-04T20:45:24.3230489Z anyascii==0.3.2 2025-03-04T20:45:24.3230685Z astroid==3.3.8 2025-03-04T20:45:24.3230870Z asttokens==3.0.0 2025-03-04T20:45:24.3231058Z astunparse==1.6.3 2025-03-04T20:45:24.3231250Z async-timeout==5.0.1 2025-03-04T20:45:24.3231442Z attrs==23.1.0 2025-03-04T20:45:24.3231623Z audioread==3.0.1 2025-03-04T20:45:24.3231804Z babel==2.17.0 2025-03-04T20:45:24.3231983Z backcall==0.2.0 2025-03-04T20:45:24.3232170Z beautifulsoup4==4.13.3 2025-03-04T20:45:24.3232753Z -e git+https://github.com/pytorch/benchmark@373ffb19dc470f4423a3176a4133f8f4b3cdb5bd#egg=bert_pytorch&subdirectory=torchbenchmark/models/BERT_pytorch 2025-03-04T20:45:24.3233366Z black==25.1.0 2025-03-04T20:45:24.3233542Z blinker==1.9.0 2025-03-04T20:45:24.3233702Z blis==1.2.0 2025-03-04T20:45:24.3233869Z blobfile==3.0.0 2025-03-04T20:45:24.3234038Z bokeh==3.4.3 2025-03-04T20:45:24.3234208Z boto3==1.35.42 2025-03-04T20:45:24.3234375Z botocore==1.35.99 2025-03-04T20:45:24.3234549Z breathe==4.34.0 2025-03-04T20:45:24.3234715Z bs4==0.0.1 2025-03-04T20:45:24.3234883Z cachetools==5.5.2 2025-03-04T20:45:24.3235117Z cardboardlint==1.3.1 2025-03-04T20:45:24.3235314Z catalogue==2.0.10 2025-03-04T20:45:24.3235492Z certifi==2025.1.31 2025-03-04T20:45:24.3235667Z cffi==1.17.1 2025-03-04T20:45:24.3235845Z charset-normalizer==3.4.1 2025-03-04T20:45:24.3236032Z click==8.1.8 2025-03-04T20:45:24.3236205Z cloudpathlib==0.21.0 2025-03-04T20:45:24.3236389Z cloudpickle==3.1.1 2025-03-04T20:45:24.3236565Z colorama==0.4.6 2025-03-04T20:45:24.3236734Z comm==0.2.2 2025-03-04T20:45:24.3236902Z confection==0.1.5 2025-03-04T20:45:24.3237074Z contourpy==1.2.1 2025-03-04T20:45:24.3237253Z coremltools==5.0b5 2025-03-04T20:45:24.3237444Z cryptography==44.0.1 2025-03-04T20:45:24.3237624Z csvw==3.5.1 2025-03-04T20:45:24.3237789Z cycler==0.12.1 2025-03-04T20:45:24.3237956Z cymem==2.0.11 2025-03-04T20:45:24.3238122Z Cython==3.0.12 2025-03-04T20:45:24.3238293Z DALL-E==0.1 2025-03-04T20:45:24.3238461Z dataclasses-json==0.6.7 2025-03-04T20:45:24.3238672Z datasets==3.3.2 2025-03-04T20:45:24.3238849Z debugpy==1.8.12 2025-03-04T20:45:24.3239019Z decorator==5.2.1 2025-03-04T20:45:24.3239193Z defusedxml==0.7.1 2025-03-04T20:45:24.3239371Z Deprecated==1.2.18 2025-03-04T20:45:24.3239727Z detectron2 @ git+https://github.com/facebookresearch/detectron2.git@0df2d73d0013db7de629602c23cc120219b4f2b8 2025-03-04T20:45:24.3240097Z diffusers==0.30.3 2025-03-04T20:45:24.3240295Z dill==0.3.8 2025-03-04T20:45:24.3240508Z diskcache==5.6.3 2025-03-04T20:45:24.3240724Z distro==1.9.0 2025-03-04T20:45:24.3240900Z dlinfo==2.0.0 2025-03-04T20:45:24.3241072Z dnspython==2.7.0 2025-03-04T20:45:24.3241257Z docker-pycreds==0.4.0 2025-03-04T20:45:24.3241452Z docutils==0.16 2025-03-04T20:45:24.3241628Z dominate==2.9.1 2025-03-04T20:45:24.3241982Z effdet @ git+https://github.com/rwightman/efficientdet-pytorch.git@d43c9e34cd62d22b4205831bb735f6dd83b8e881 2025-03-04T20:45:24.3242376Z einops==0.8.1 2025-03-04T20:45:24.3242558Z eval_type_backport==0.2.2 2025-03-04T20:45:24.3242763Z exceptiongroup==1.2.2 2025-03-04T20:45:24.3243032Z execnet==2.1.1 2025-03-04T20:45:24.3243206Z executing==2.2.0 2025-03-04T20:45:24.3243370Z exhale==0.2.3 2025-03-04T20:45:24.3243552Z expecttest==0.3.0 2025-03-04T20:45:24.3243732Z fastjsonschema==2.21.1 2025-03-04T20:45:24.3243918Z FastNLP==0.6.0 2025-03-04T20:45:24.3244091Z fbscribelogger==0.1.7 2025-03-04T20:45:24.3244279Z ffmpeg-python==0.2.0 2025-03-04T20:45:24.3244463Z filelock==3.16.1 2025-03-04T20:45:24.3244635Z Flask==3.1.0 2025-03-04T20:45:24.3244798Z flatbuffers==2.0 2025-03-04T20:45:24.3244968Z fonttools==4.56.0 2025-03-04T20:45:24.3245136Z frozenlist==1.5.0 2025-03-04T20:45:24.3245340Z fsspec==2024.10.0 2025-03-04T20:45:24.3245509Z ftfy==6.3.1 2025-03-04T20:45:24.3245805Z functorch @ git+https://github.com/pytorch/functorch.git@b71aa0b4387b86c278132209b99538be48ef4c74 2025-03-04T20:45:24.3246131Z future==1.0.0 2025-03-04T20:45:24.3246309Z fvcore==0.1.5.post20221221 2025-03-04T20:45:24.3246500Z gdown==5.2.0 2025-03-04T20:45:24.3246659Z ghstack==0.8.0 2025-03-04T20:45:24.3246827Z gitdb==4.0.12 2025-03-04T20:45:24.3247076Z GitPython==3.1.44 2025-03-04T20:45:24.3247264Z google-auth==2.38.0 2025-03-04T20:45:24.3247460Z google-auth-oauthlib==1.0.0 2025-03-04T20:45:24.3247665Z greenlet==3.1.1 2025-03-04T20:45:24.3247839Z grpcio==1.70.0 2025-03-04T20:45:24.3248017Z gym==0.26.2 2025-03-04T20:45:24.3248185Z gym-notices==0.0.8 2025-03-04T20:45:24.3248357Z h5py==3.13.0 2025-03-04T20:45:24.3248518Z higher==0.2.1 2025-03-04T20:45:24.3248686Z huggingface-hub==0.29.1 2025-03-04T20:45:24.3248873Z hydra-core==1.3.2 2025-03-04T20:45:24.3249044Z hypothesis==5.35.1 2025-03-04T20:45:24.3249208Z idna==3.10 2025-03-04T20:45:24.3249367Z imageio==2.37.0 2025-03-04T20:45:24.3249593Z imagesize==1.4.1 2025-03-04T20:45:24.3249773Z importlib_metadata==8.6.1 2025-03-04T20:45:24.3249966Z inflect==7.5.0 2025-03-04T20:45:24.3250137Z iniconfig==2.0.0 2025-03-04T20:45:24.3250311Z iopath==0.1.9 2025-03-04T20:45:24.3250486Z ipykernel==6.29.5 2025-03-04T20:45:24.3250667Z ipython==8.12.0 2025-03-04T20:45:24.3250845Z isodate==0.7.2 2025-03-04T20:45:24.3251021Z isort==6.0.1 2025-03-04T20:45:24.3251197Z itsdangerous==2.2.0 2025-03-04T20:45:24.3251378Z jedi==0.19.2 2025-03-04T20:45:24.3251542Z Jinja2==3.1.5 2025-03-04T20:45:24.3251713Z jmespath==1.0.1 2025-03-04T20:45:24.3251892Z joblib==1.4.2 2025-03-04T20:45:24.3252067Z jsonpatch==1.33 2025-03-04T20:45:24.3252248Z jsonpointer==3.0.0 2025-03-04T20:45:24.3252434Z jsonschema==4.23.0 2025-03-04T20:45:24.3252636Z jsonschema-specifications==2024.10.1 2025-03-04T20:45:24.3252865Z junitparser==2.1.1 2025-03-04T20:45:24.3253145Z jupyter-cache==0.6.1 2025-03-04T20:45:24.3253355Z jupyter_client==8.6.3 2025-03-04T20:45:24.3253553Z jupyter_core==5.7.2 2025-03-04T20:45:24.3253738Z kaldi-io==0.9.8 2025-03-04T20:45:24.3253916Z kiwisolver==1.4.7 2025-03-04T20:45:24.3254088Z kornia==0.8.0 2025-03-04T20:45:24.3254262Z kornia_rs==0.1.8 2025-03-04T20:45:24.3254445Z lameenc==1.8.1 2025-03-04T20:45:24.3254623Z langcodes==3.5.0 2025-03-04T20:45:24.3254806Z langdetect==1.0.9 2025-03-04T20:45:24.3254992Z language-tags==1.2.0 2025-03-04T20:45:24.3255192Z language_data==1.3.0 2025-03-04T20:45:24.3255373Z lark==0.12.0 2025-03-04T20:45:24.3255551Z lazy_loader==0.4 2025-03-04T20:45:24.3255733Z libcst==1.6.0 2025-03-04T20:45:24.3255905Z librosa==0.9.2 2025-03-04T20:45:24.3256079Z lintrunner==0.12.7 2025-03-04T20:45:24.3256256Z llvmlite==0.38.1 2025-03-04T20:45:24.3256435Z lxml==5.3.0 2025-03-04T20:45:24.3256599Z marisa-trie==1.2.1 2025-03-04T20:45:24.3256782Z Markdown==3.7 2025-03-04T20:45:24.3257058Z markdown-it-py==2.2.0 2025-03-04T20:45:24.3257272Z MarkupSafe==3.0.2 2025-03-04T20:45:24.3257465Z marshmallow==3.26.1 2025-03-04T20:45:24.3257673Z matplotlib==3.5.3 2025-03-04T20:45:24.3257880Z matplotlib-inline==0.1.7 2025-03-04T20:45:24.3258095Z mccabe==0.7.0 2025-03-04T20:45:24.3258290Z mdit-py-plugins==0.3.5 2025-03-04T20:45:24.3258494Z mdurl==0.1.2 2025-03-04T20:45:24.3258681Z ml_dtypes==0.5.1 2025-03-04T20:45:24.3258873Z MonkeyType==23.3.0 2025-03-04T20:45:24.3259078Z more-itertools==10.6.0 2025-03-04T20:45:24.3259263Z mpmath==1.3.0 2025-03-04T20:45:24.3259447Z msgpack==1.1.0 2025-03-04T20:45:24.3259634Z multidict==6.1.0 2025-03-04T20:45:24.3259828Z multiprocess==0.70.16 2025-03-04T20:45:24.3260030Z murmurhash==1.0.12 2025-03-04T20:45:24.3260218Z musdb==0.4.2 2025-03-04T20:45:24.3260405Z museval==0.4.1 2025-03-04T20:45:24.3260598Z mypy==1.13.0 2025-03-04T20:45:24.3260790Z mypy-extensions==1.0.0 2025-03-04T20:45:24.3260997Z myst-nb==0.17.2 2025-03-04T20:45:24.3261187Z myst-parser==0.18.1 2025-03-04T20:45:24.3261383Z nbclient==0.7.4 2025-03-04T20:45:24.3261572Z nbformat==5.10.4 2025-03-04T20:45:24.3261758Z nest-asyncio==1.6.0 2025-03-04T20:45:24.3261949Z networkx==2.8.8 2025-03-04T20:45:24.3262138Z ninja==1.10.2 2025-03-04T20:45:24.3262321Z nose==1.3.7 2025-03-04T20:45:24.3262498Z numba==0.55.2 2025-03-04T20:45:24.3262680Z numpy==1.22.4 2025-03-04T20:45:24.3262874Z nvidia-cublas-cu12==12.4.5.8 2025-03-04T20:45:24.3263116Z nvidia-cuda-cupti-cu12==12.4.127 2025-03-04T20:45:24.3263371Z nvidia-cuda-nvrtc-cu12==12.4.127 2025-03-04T20:45:24.3263682Z nvidia-cuda-runtime-cu12==12.4.127 2025-03-04T20:45:24.3263923Z nvidia-cudnn-cu12==9.1.0.70 2025-03-04T20:45:24.3264138Z nvidia-cufft-cu12==11.2.1.3 2025-03-04T20:45:24.3264351Z nvidia-curand-cu12==10.3.5.147 2025-03-04T20:45:24.3264569Z nvidia-cusolver-cu12==11.6.1.9 2025-03-04T20:45:24.3264791Z nvidia-cusparse-cu12==12.3.1.170 2025-03-04T20:45:24.3265008Z nvidia-cusparselt-cu12==0.6.2 2025-03-04T20:45:24.3265226Z nvidia-ml-py==12.570.86 2025-03-04T20:45:24.3265428Z nvidia-nccl-cu12==2.21.5 2025-03-04T20:45:24.3265639Z nvidia-nvjitlink-cu12==12.4.127 2025-03-04T20:45:24.3265855Z nvidia-nvtx-cu12==12.4.127 2025-03-04T20:45:24.3266106Z oauthlib==3.2.2 2025-03-04T20:45:24.3266285Z omegaconf==2.3.0 2025-03-04T20:45:24.3266461Z onnx==1.17.0 2025-03-04T20:45:24.3266637Z onnxscript==0.1.0 2025-03-04T20:45:24.3266816Z opacus==1.5.3 2025-03-04T20:45:24.3266994Z opencv-python==4.8.0.74 2025-03-04T20:45:24.3267185Z opt-einsum==3.3.0 2025-03-04T20:45:24.3267368Z optree==0.13.0 2025-03-04T20:45:24.3267546Z packaging==24.2 2025-03-04T20:45:24.3267724Z pandas==2.0.3 2025-03-04T20:45:24.3267907Z parameterized==0.8.1 2025-03-04T20:45:24.3268108Z parso==0.8.4 2025-03-04T20:45:24.3268277Z patch==1.16 2025-03-04T20:45:24.3268448Z pathspec==0.12.1 2025-03-04T20:45:24.3268624Z pexpect==4.9.0 2025-03-04T20:45:24.3268803Z phonemizer==3.3.0 2025-03-04T20:45:24.3268992Z pickleshare==0.7.5 2025-03-04T20:45:24.3269177Z pillow==11.0.0 2025-03-04T20:45:24.3269355Z platformdirs==4.3.6 2025-03-04T20:45:24.3269538Z pluggy==1.5.0 2025-03-04T20:45:24.3269709Z ply==3.11 2025-03-04T20:45:24.3269879Z pooch==1.8.2 2025-03-04T20:45:24.3270056Z portalocker==3.1.1 2025-03-04T20:45:24.3270238Z preshed==3.0.9 2025-03-04T20:45:24.3270412Z prettytable==3.15.1 2025-03-04T20:45:24.3270593Z prompt_toolkit==3.0.50 2025-03-04T20:45:24.3270778Z propcache==0.3.0 2025-03-04T20:45:24.3270956Z protobuf==3.20.2 2025-03-04T20:45:24.3271132Z psutil==7.0.0 2025-03-04T20:45:24.3271306Z ptyprocess==0.7.0 2025-03-04T20:45:24.3271483Z PuLP==2.9.0 2025-03-04T20:45:24.3271664Z pure_eval==0.2.3 2025-03-04T20:45:24.3271838Z pwlf==2.2.1 2025-03-04T20:45:24.3272009Z py-cpuinfo==9.0.0 2025-03-04T20:45:24.3272185Z pyaml==25.1.0 2025-03-04T20:45:24.3272347Z pyarrow==19.0.1 2025-03-04T20:45:24.3272523Z pyasn1==0.6.1 2025-03-04T20:45:24.3272699Z pyasn1_modules==0.4.1 2025-03-04T20:45:24.3272892Z pyclipper==1.3.0.post6 2025-03-04T20:45:24.3273080Z pycocotools==2.0.8 2025-03-04T20:45:24.3273256Z pycparser==2.22 2025-03-04T20:45:24.3273434Z pycryptodomex==3.21.0 2025-03-04T20:45:24.3273620Z pydantic==2.10.6 2025-03-04T20:45:24.3273800Z pydantic_core==2.27.2 2025-03-04T20:45:24.3273981Z pyDOE==0.3.8 2025-03-04T20:45:24.3274156Z pydot==3.0.4 2025-03-04T20:45:24.3274323Z pygame==2.6.1 2025-03-04T20:45:24.3274493Z PyGithub==2.3.0 2025-03-04T20:45:24.3274672Z Pygments==2.15.0 2025-03-04T20:45:24.3274850Z PyJWT==2.10.1 2025-03-04T20:45:24.3275020Z pylint==3.3.4 2025-03-04T20:45:24.3275193Z PyNaCl==1.5.0 2025-03-04T20:45:24.3275376Z pynvml==12.0.0 2025-03-04T20:45:24.3275556Z pyparsing==3.2.1 2025-03-04T20:45:24.3275742Z pypdfium2==4.30.1 2025-03-04T20:45:24.3275932Z pysbd==0.3.4 2025-03-04T20:45:24.3276102Z PySocks==1.7.1 2025-03-04T20:45:24.3276273Z pytest==8.3.5 2025-03-04T20:45:24.3276452Z pytest-benchmark==5.1.0 2025-03-04T20:45:24.3276645Z pytest-cpp==2.3.0 2025-03-04T20:45:24.3276834Z pytest-flakefinder==1.1.0 2025-03-04T20:45:24.3277047Z pytest-rerunfailures==14.0 2025-03-04T20:45:24.3277248Z pytest-subtests==0.13.1 2025-03-04T20:45:24.3277439Z pytest-xdist==3.3.1 2025-03-04T20:45:24.3277630Z python-dateutil==2.9.0.post0 2025-03-04T20:45:24.3277835Z python-doctr==0.10.0 2025-03-04T20:45:24.3278022Z python-etcd==0.4.5 2025-03-04T20:45:24.3278489Z pytorch-labs-segment-anything-fast @ git+https://github.com/pytorch-labs/segment-anything-fast.git@e6aadeb86f3ae1f58c3f98e2a91e251716e0f2aa 2025-03-04T20:45:24.3279169Z -e git+https://github.com/pytorch/pytorch_sphinx_theme.git@4125c834e1aa0945fde6ef58ff2f77f7abedc460#egg=pytorch_sphinx_theme 2025-03-04T20:45:24.3279587Z pytz==2025.1 2025-03-04T20:45:24.3279834Z PyWavelets==1.4.1 2025-03-04T20:45:24.3280022Z PyYAML==6.0.2 2025-03-04T20:45:24.3280199Z pyzmq==26.2.1 2025-03-04T20:45:24.3280372Z pyzstd==0.16.2 2025-03-04T20:45:24.3280550Z RapidFuzz==3.12.2 2025-03-04T20:45:24.3290802Z rdflib==7.1.3 2025-03-04T20:45:24.3291034Z redis==5.2.1 2025-03-04T20:45:24.3291261Z referencing==0.36.2 2025-03-04T20:45:24.3291448Z regex==2024.11.6 2025-03-04T20:45:24.3291628Z requests==2.32.3 2025-03-04T20:45:24.3291820Z requests-oauthlib==2.0.0 2025-03-04T20:45:24.3292018Z resampy==0.4.3 2025-03-04T20:45:24.3292195Z rfc3986==1.5.0 2025-03-04T20:45:24.3292364Z rich==13.9.4 2025-03-04T20:45:24.3292787Z rpds-py==0.23.1 2025-03-04T20:45:24.3292966Z rsa==4.9 2025-03-04T20:45:24.3293136Z s3transfer==0.10.4 2025-03-04T20:45:24.3293318Z safetensors==0.5.3 2025-03-04T20:45:24.3293500Z scikit-image==0.19.3 2025-03-04T20:45:24.3293691Z scikit-learn==1.6.1 2025-03-04T20:45:24.3293871Z scipy==1.10.1 2025-03-04T20:45:24.3294043Z segments==2.3.0 2025-03-04T20:45:24.3294233Z sentencepiece==0.2.0 2025-03-04T20:45:24.3294423Z sentry-sdk==2.22.0 2025-03-04T20:45:24.3294597Z setproctitle==1.3.5 2025-03-04T20:45:24.3294782Z shapely==2.0.7 2025-03-04T20:45:24.3294959Z shellingham==1.5.4 2025-03-04T20:45:24.3295146Z simplejson==3.20.1 2025-03-04T20:45:24.3295325Z six==1.17.0 2025-03-04T20:45:24.3295499Z smart-open==7.1.0 2025-03-04T20:45:24.3295685Z smmap==5.0.2 2025-03-04T20:45:24.3295867Z snowballstemmer==2.2.0 2025-03-04T20:45:24.3296067Z sortedcontainers==2.4.0 2025-03-04T20:45:24.3296266Z soundfile==0.13.1 2025-03-04T20:45:24.3296447Z soupsieve==2.6 2025-03-04T20:45:24.3296626Z soxr==0.5.0.post1 2025-03-04T20:45:24.3296809Z spacy==3.8.3 2025-03-04T20:45:24.3297099Z spacy-legacy==3.0.12 2025-03-04T20:45:24.3297297Z spacy-loggers==1.0.5 2025-03-04T20:45:24.3297498Z Sphinx==5.3.0 2025-03-04T20:45:24.3297699Z sphinx-copybutton==0.5.0 2025-03-04T20:45:24.3297918Z sphinx-panels==0.4.1 2025-03-04T20:45:24.3298138Z sphinxcontrib-applehelp==2.0.0 2025-03-04T20:45:24.3298390Z sphinxcontrib-devhelp==2.0.0 2025-03-04T20:45:24.3298640Z sphinxcontrib-htmlhelp==2.1.0 2025-03-04T20:45:24.3298863Z sphinxcontrib-jsmath==1.0.1 2025-03-04T20:45:24.3299081Z sphinxcontrib-katex==0.8.6 2025-03-04T20:45:24.3299297Z sphinxcontrib-qthelp==2.0.0 2025-03-04T20:45:24.3299524Z sphinxcontrib-serializinghtml==2.0.0 2025-03-04T20:45:24.3299759Z SQLAlchemy==2.0.38 2025-03-04T20:45:24.3299939Z srsly==2.5.1 2025-03-04T20:45:24.3300116Z stack-data==0.6.3 2025-03-04T20:45:24.3300295Z stempeg==0.2.3 2025-03-04T20:45:24.3300474Z submitit==1.5.2 2025-03-04T20:45:24.3300653Z sympy==1.13.3 2025-03-04T20:45:24.3300827Z tabulate==0.9.0 2025-03-04T20:45:24.3301012Z tb-nightly==2.13.0a20230426 2025-03-04T20:45:24.3301216Z tensorboard==2.13.0 2025-03-04T20:45:24.3301411Z tensorboard-data-server==0.7.2 2025-03-04T20:45:24.3301624Z tensorboardX==2.6.2.2 2025-03-04T20:45:24.3301815Z termcolor==2.5.0 2025-03-04T20:45:24.3302015Z thinc==8.3.4 2025-03-04T20:45:24.3302191Z threadpoolctl==3.5.0 2025-03-04T20:45:24.3302381Z thriftpy2==0.5.2 2025-03-04T20:45:24.3302565Z tifffile==2024.8.30 2025-03-04T20:45:24.3302945Z timm @ git+https://github.com/huggingface/pytorch-image-models.git@730b907b4d45a4713cbc425cbf224c46089fd514 2025-03-04T20:45:24.3303326Z tlparse==0.3.30 2025-03-04T20:45:24.3303507Z tokenizers==0.15.2 2025-03-04T20:45:24.3303679Z tomli==2.2.1 2025-03-04T20:45:24.3303849Z tomlkit==0.13.2 2025-03-04T20:45:24.3304363Z torch @ file:///var/lib/jenkins/workspace/dist/torch-2.7.0a0%2Bgit1b74980-cp39-cp39-linux_x86_64.whl#sha256=dc833e86ed3bb70ba05ba214f76e8bf0f1fc74304f0d390f2a2591a3606fe7c6 2025-03-04T20:45:24.3305075Z torch_geometric @ git+https://github.com/pyg-team/pytorch_geometric.git@cabcd4097442ba60aa1efa11e1619dd9bb8fb527 2025-03-04T20:45:24.3305588Z torchao @ git+https://github.com/pytorch/ao.git@9bcd73be6fb60cc169deeaf5b5508cb4fdaefcb5 2025-03-04T20:45:24.3306049Z torchaudio @ git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8 2025-03-04T20:45:24.3306671Z torchmultimodal @ git+https://github.com/facebookresearch/multimodal.git@6569fcc03450c2360b50d772bf9b18ec3487fcf4 2025-03-04T20:45:24.3307205Z torchvision @ git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T20:45:24.3307548Z tornado==6.4.2 2025-03-04T20:45:24.3307732Z tqdm==4.67.1 2025-03-04T20:45:24.3307910Z traitlets==5.14.3 2025-03-04T20:45:24.3308096Z transformers==4.38.1 2025-03-04T20:45:24.3308285Z treetable==0.2.5 2025-03-04T20:45:24.3308497Z triton @ file:///var/lib/jenkins/triton/python 2025-03-04T20:45:24.3308741Z typeguard==4.4.2 2025-03-04T20:45:24.3308924Z typer==0.15.2 2025-03-04T20:45:24.3309108Z typing-inspect==0.9.0 2025-03-04T20:45:24.3309352Z typing_extensions==4.12.2 2025-03-04T20:45:24.3309550Z tzdata==2025.1 2025-03-04T20:45:24.3309725Z Unidecode==1.3.8 2025-03-04T20:45:24.3309911Z unittest-xml-reporting==3.2.0 2025-03-04T20:45:24.3310125Z uritemplate==4.1.1 2025-03-04T20:45:24.3310307Z urllib3==1.26.20 2025-03-04T20:45:24.3310482Z visdom==0.2.4 2025-03-04T20:45:24.3310662Z wandb==0.19.7 2025-03-04T20:45:24.3310833Z wasabi==1.1.3 2025-03-04T20:45:24.3310999Z wcwidth==0.2.13 2025-03-04T20:45:24.3311170Z weasel==0.4.1 2025-03-04T20:45:24.3311343Z websocket-client==1.8.0 2025-03-04T20:45:24.3311531Z Werkzeug==3.1.3 2025-03-04T20:45:24.3311702Z wrapt==1.17.2 2025-03-04T20:45:24.3311869Z xdoctest==1.1.0 2025-03-04T20:45:24.3312039Z xxhash==3.5.0 2025-03-04T20:45:24.3312204Z xyzservices==2025.1.0 2025-03-04T20:45:24.3312386Z yacs==0.1.8 2025-03-04T20:45:24.3312554Z yarl==1.18.3 2025-03-04T20:45:24.3312724Z z3-solver==4.12.6.0 2025-03-04T20:45:24.3312897Z zipp==3.21.0 2025-03-04T20:45:24.3652364Z + popd 2025-03-04T20:45:24.3655250Z ~/workspace 2025-03-04T20:45:24.3655541Z + [[ dynamic_cpu_inductor_torchbench != *cpu* ]] 2025-03-04T20:45:24.3655859Z ++ pwd 2025-03-04T20:45:24.3656109Z + PYTHONPATH=/var/lib/jenkins/workspace/torchbench 2025-03-04T20:45:24.3656436Z + test_dynamo_benchmark torchbench 0 2025-03-04T20:45:24.3656705Z ++ pwd 2025-03-04T20:45:24.3657087Z + TEST_REPORTS_DIR=/var/lib/jenkins/workspace/test/test-reports 2025-03-04T20:45:24.3657460Z + local suite=torchbench 2025-03-04T20:45:24.3657666Z + shift 2025-03-04T20:45:24.3657851Z + local shard_id=0 2025-03-04T20:45:24.3658069Z + shift 2025-03-04T20:45:24.3658385Z + [[ dynamic_cpu_inductor_torchbench == *perf_compare* ]] 2025-03-04T20:45:24.3658728Z + [[ dynamic_cpu_inductor_torchbench == *perf* ]] 2025-03-04T20:45:24.3659047Z + [[ dynamic_cpu_inductor_torchbench == *cpu* ]] 2025-03-04T20:45:24.3659337Z + local dt=float32 2025-03-04T20:45:24.3659595Z + [[ dynamic_cpu_inductor_torchbench == *amp* ]] 2025-03-04T20:45:24.3659933Z + [[ dynamic_cpu_inductor_torchbench == *freezing* ]] 2025-03-04T20:45:24.3660298Z + test_single_dynamo_benchmark inference torchbench 0 --inference --float32 2025-03-04T20:45:24.3662437Z ++ pwd 2025-03-04T20:45:24.3662800Z + TEST_REPORTS_DIR=/var/lib/jenkins/workspace/test/test-reports 2025-03-04T20:45:24.3663144Z + mkdir -p /var/lib/jenkins/workspace/test/test-reports 2025-03-04T20:45:24.3713749Z + local name=inference 2025-03-04T20:45:24.3714104Z + shift 2025-03-04T20:45:24.3714325Z + local suite=torchbench 2025-03-04T20:45:24.3714553Z + shift 2025-03-04T20:45:24.3714773Z + local shard_id=0 2025-03-04T20:45:24.3715002Z + shift 2025-03-04T20:45:24.3715207Z + partition_flags=() 2025-03-04T20:45:24.3715443Z + local partition_flags 2025-03-04T20:45:24.3715673Z + [[ -n 2 ]] 2025-03-04T20:45:24.3715890Z + [[ -n 0 ]] 2025-03-04T20:45:24.3716233Z + partition_flags=(--total-partitions "$NUM_TEST_SHARDS" --partition-id "$shard_id") 2025-03-04T20:45:24.3716681Z + [[ dynamic_cpu_inductor_torchbench == *perf_compare* ]] 2025-03-04T20:45:24.3717006Z + [[ dynamic_cpu_inductor_torchbench == *perf* ]] 2025-03-04T20:45:24.3717321Z + [[ dynamic_cpu_inductor_torchbench == *_avx2* ]] 2025-03-04T20:45:24.3717648Z + [[ dynamic_cpu_inductor_torchbench == *_avx512* ]] 2025-03-04T20:45:24.3718856Z + python benchmarks/dynamo/torchbench.py --ci --accuracy --timing --explain --print-compilation-time --inductor --dynamic-shapes --dynamic-batch-only --device cpu --inference --float32 --total-partitions 2 --partition-id 0 --output /var/lib/jenkins/workspace/test/test-reports/inference_torchbench.csv 2025-03-04T20:45:29.3957505Z 2025-03-04T20:45:31.3036556Z loading model: 0it [00:00, ?it/s] 2025-03-04T20:45:31.3037968Z loading model: 0it [00:01, ?it/s] 2025-03-04T20:45:31.3051615Z cpu eval BERT_pytorch 2025-03-04T20:45:32.1778446Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:45:32.4917820Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:45:32.8186434Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:47:00.0501564Z Compilation time (from dynamo_timed): 85.638759276 2025-03-04T20:47:00.0558630Z pass 2025-03-04T20:47:00.0563120Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:47:00.0568048Z TIMING: _recursive_pre_grad_passes:0.00824 _recursive_joint_graph_passes:0.70773 _recursive_post_grad_passes:0.07717 async_compile.wait:1.38714 code_gen:13.86512 inductor_compile:28.42345 backend_compile:71.66679 entire_frame_compile:85.63876 gc:0.00057 total_wall_time:85.63876 2025-03-04T20:47:00.0570643Z STATS: call_* op count: 543 | FakeTensor.__torch_dispatch__:1424 | FakeTensorMode.__torch_dispatch__:25598 | attempt fast:1528 | fast is_contiguous:1528 | ProxyTorchDispatchMode.__torch_dispatch__:6258 2025-03-04T20:47:00.0571425Z Dynamo produced 1 graphs covering 543 ops with 0 graph breaks (0 unique) 2025-03-04T20:47:43.5871983Z 2025-03-04T20:47:46.8222014Z loading model: 0it [00:00, ?it/s] 2025-03-04T20:47:46.8224983Z loading model: 0it [00:03, ?it/s] 2025-03-04T20:47:46.8233190Z cpu eval Background_Matting 2025-03-04T20:47:46.8597358Z Compilation time (from dynamo_timed): 0 2025-03-04T20:47:46.8597664Z pass_due_to_skip 2025-03-04T20:47:46.8598024Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:47:46.8598633Z TIMING: total_wall_time:0 2025-03-04T20:47:46.8598909Z STATS: call_* op count: 0 2025-03-04T20:47:46.8599325Z Dynamo produced 0 graphs covering 0 ops with 0 graph breaks (0 unique) 2025-03-04T20:47:50.1548179Z 2025-03-04T20:47:51.9823890Z loading model: 0it [00:00, ?it/s] 2025-03-04T20:47:51.9824305Z loading model: 0it [00:01, ?it/s] 2025-03-04T20:47:51.9826839Z cpu eval LearningToPaint 2025-03-04T20:47:52.0746918Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:47:52.1092999Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:47:52.1351797Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:48:11.1828678Z Compilation time (from dynamo_timed): 18.124312836 2025-03-04T20:48:11.1833598Z pass 2025-03-04T20:48:11.1838403Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:48:11.1843351Z TIMING: _recursive_pre_grad_passes:0.0049 _recursive_joint_graph_passes:0.10736 _recursive_post_grad_passes:0.02749 async_compile.wait:1.33114 code_gen:8.10526 inductor_compile:10.9381 backend_compile:15.77506 entire_frame_compile:18.12431 gc:0.00134 total_wall_time:18.12431 2025-03-04T20:48:11.1845104Z STATS: call_* op count: 71 | FakeTensorMode.__torch_dispatch__:6408 | attempt fast:368 | fast is_contiguous:368 | ProxyTorchDispatchMode.__torch_dispatch__:1902 | FakeTensor.__torch_dispatch__:590 2025-03-04T20:48:11.1845792Z Dynamo produced 1 graphs covering 71 ops with 0 graph breaks (0 unique) 2025-03-04T20:48:18.6174278Z 2025-03-04T20:48:19.4759586Z loading model: 0it [00:00, ?it/s]Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/vgg16-397923af.pth 2025-03-04T20:48:19.5027892Z 2025-03-04T20:48:19.5029191Z 2025-03-04T20:48:19.6059231Z 0% 0.00/528M [00:00, code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:59:31.1247010Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:59:31.1247157Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:59:31.1247233Z 2025-03-04T20:59:31.1247708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:59:31.1247889Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75); x_193 = None 2025-03-04T20:59:31.1248075Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:59:31.1248271Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:59:31.1248340Z 2025-03-04T20:59:31.1248801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:59:31.1249020Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:59:31.1249095Z 2025-03-04T20:59:31.1249552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:59:31.1249715Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:59:31.1249870Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:59:31.1250027Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:59:31.1250097Z 2025-03-04T20:59:31.1250495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:59:31.1250677Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:59:31.1250756Z 2025-03-04T20:59:31.1251083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:59:31.1251238Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:59:31.1251307Z 2025-03-04T20:59:31.1251646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:59:31.1251784Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:59:31.1251929Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:59:31.1252083Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:59:31.1252161Z 2025-03-04T20:59:31.1252499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:59:31.1252635Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:59:31.1252763Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:59:31.1253001Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:59:31.1253077Z 2025-03-04T20:59:31.1254347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:59:31.1254501Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:59:31.1254597Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:59:31.1254729Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:59:31.1254808Z 2025-03-04T20:59:31.1255217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:59:31.1255380Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:59:31.1255486Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:59:31.1255624Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:59:31.1255701Z 2025-03-04T20:59:31.1256083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:59:31.1256285Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:59:31.1256410Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:59:31.1256490Z 2025-03-04T20:59:31.1256811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:59:31.1256978Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:59:31.1257418Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:59:31.1257505Z 2025-03-04T20:59:31.1257828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:59:31.1258004Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:59:31.1258124Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:59:31.1258203Z 2025-03-04T20:59:31.1258528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:59:31.1258733Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:59:31.1258853Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:59:31.1258933Z 2025-03-04T20:59:31.1259293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:59:31.1259455Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:59:31.1259528Z 2025-03-04T20:59:31.1259896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:59:31.1260044Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:59:31.1260124Z 2025-03-04T20:59:31.1260496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:59:31.1260655Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:59:31.1260789Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:59:31.1260961Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:59:31.1261114Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:59:31.1261190Z 2025-03-04T20:59:31.1261561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:59:31.1262161Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:59:31.1262308Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:59:31.1262469Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:59:31.1262621Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:59:31.1262690Z 2025-03-04T20:59:31.1263053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:59:31.1263226Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:59:31.1263406Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:59:31.1263546Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:59:31.1263621Z 2025-03-04T20:59:31.1263970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:59:31.1264100Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:59:31.1264274Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:59:31.1264423Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:59:31.1264496Z 2025-03-04T20:59:31.1264835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:59:31.1264937Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:59:31.1265075Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:59:31.1265144Z 2025-03-04T20:59:31.1265479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:59:31.1265578Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:59:31.1265708Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:59:31.1265776Z 2025-03-04T20:59:31.1266106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:59:31.1266228Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:59:31.1266369Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:59:31.1266439Z 2025-03-04T20:59:31.1266767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:59:31.1266884Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:59:31.1267023Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:59:31.1267091Z 2025-03-04T20:59:31.1267929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:59:31.1268137Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:59:31.1268216Z 2025-03-04T20:59:31.1268563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:59:31.1268778Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:59:31.1268848Z 2025-03-04T20:59:31.1269249Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:59:31.1269428Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:59:31.1269504Z 2025-03-04T20:59:31.1270000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:59:31.1270187Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:59:31.1270254Z 2025-03-04T20:59:31.1270568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:31.1270712Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:59:31.1270786Z 2025-03-04T20:59:31.1271236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:59:31.1271363Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:59:31.1271472Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:59:31.1271602Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:59:31.1271669Z 2025-03-04T20:59:31.1272152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:59:31.1272329Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:59:31.1272570Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:59:31.1272646Z 2025-03-04T20:59:31.1273117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:59:31.1273300Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:59:31.1273368Z 2025-03-04T20:59:31.1273682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:31.1273841Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:59:31.1273918Z 2025-03-04T20:59:31.1274310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:59:31.1274471Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:59:31.1274538Z 2025-03-04T20:59:31.1274854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:59:31.1275006Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:59:31.1275080Z 2025-03-04T20:59:31.1276083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:59:31.1276247Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:59:31.1276316Z 2025-03-04T20:59:31.1276820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:59:31.1276965Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:59:31.1277131Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:59:31.1277294Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:59:31.1277439Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:59:31.1277507Z 2025-03-04T20:59:31.1277894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:59:31.1278015Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:59:31.1278093Z 2025-03-04T20:59:31.1278666Z 2025-03-04T20:59:31.1278764Z class GraphModule(torch.nn.Module): 2025-03-04T20:59:31.1373518Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", 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L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:59:31.1375639Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:59:31.1376024Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1376449Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1376872Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1377275Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1377681Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1378060Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1378487Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1378911Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1379326Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1379728Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1380084Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1380516Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1380944Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1381331Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1381715Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1382110Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1382539Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1382954Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1383375Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1383810Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1384192Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:59:31.1384630Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1385048Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1385459Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1385851Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1386176Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1386587Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1387011Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1387406Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1387794Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1388240Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1388662Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1389072Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1389473Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1389908Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1390229Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1390661Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1391064Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1391510Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1391898Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1392218Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1392632Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1393544Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1393914Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1394267Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1394587Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1394965Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1395349Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1395711Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1396062Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1396379Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1396757Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1397143Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1397496Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1397899Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1398212Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1398597Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1398972Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1399351Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1399708Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1400019Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1400405Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1400776Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1401144Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1401483Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1401813Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1402225Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1402593Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1402955Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1403295Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1403628Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:59:31.1404017Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1404412Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1404813Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1405178Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1405501Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1405875Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1406294Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1406647Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1406994Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1407305Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1407686Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1408419Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1408802Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1409152Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1409468Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1409851Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1410226Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1410591Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1410936Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1411261Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1411637Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1412058Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1412417Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1412754Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1413208Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1413696Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1414138Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1414530Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1414881Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1415222Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1415643Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1416053Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1416436Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1416816Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1417158Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1417579Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1417987Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1418379Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1418750Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1419099Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1419521Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1420585Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1420986Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1421339Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1421671Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1422056Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1422414Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1422747Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1423073Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1423377Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1423731Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1424090Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1424421Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1424748Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1425045Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1425409Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1425761Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1426104Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1426434Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1426734Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1427129Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1427480Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1427819Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1428140Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1428499Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:59:31.1428865Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1429233Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1429585Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1429921Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1430227Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1430581Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1430935Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1431263Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1431588Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1431888Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1432239Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1432586Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1432913Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1433237Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1433533Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1433930Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1434279Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1434627Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1434980Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1435267Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1435617Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1435954Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1436282Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1436595Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1436891Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1437236Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1437582Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1437908Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1438223Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1438519Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1438862Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1439206Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1439529Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1439855Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1440172Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1440527Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1440872Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1441189Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1441541Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1441829Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1442179Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1442518Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1442848Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1443161Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1443454Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1443802Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1444144Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1444472Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1444785Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1445080Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1445423Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1445769Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1446088Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1446411Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1446737Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1447082Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1447424Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1447774Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1448094Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1448377Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1448721Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1449054Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1449380Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1449702Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1449987Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1450337Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1450670Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1450997Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1451311Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1451604Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1451943Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1452285Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1452612Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1453047Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1453365Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1453727Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1454091Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1454473Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1454796Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1455084Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1455527Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1455907Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1456253Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1456606Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1456909Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1457293Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1457648Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1457999Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1458342Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1459773Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1460198Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1460584Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1461029Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1461377Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1461700Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1462077Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1462492Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1462836Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1463172Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1463490Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1463862Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1464227Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1464578Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1464916Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1465219Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1465572Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1465912Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1466243Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1466567Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1466852Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1467201Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1467540Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1467900Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1468217Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1468514Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1468856Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1469235Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1469564Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1469877Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1470169Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1470517Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1470866Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1471187Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1471505Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1471793Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1472147Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1472498Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1472820Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1473137Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1473423Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1473776Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1474146Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1474480Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1474792Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1475083Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1475464Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1475805Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1476132Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1476445Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1476747Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1477098Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1477448Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1477772Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1478099Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1478399Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1478749Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1479100Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1479426Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1479748Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1480040Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1480394Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1480769Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1481100Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1481420Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1481775Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1482128Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1482469Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1482799Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1483113Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1483410Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1483757Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1484103Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1484433Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1484744Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1485043Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1485389Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1485732Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1486062Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1486381Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1486673Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1487068Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1487428Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1487752Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1488277Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1488658Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1489018Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1490052Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1490418Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1490749Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1491064Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1491432Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1491790Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1492137Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1492470Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1492779Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1493214Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1493635Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1493984Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1494334Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1494712Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1495082Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1495459Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1495815Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1496181Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1496483Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1496843Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1497190Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1497531Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1497863Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1498163Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1498529Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1498877Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1499215Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1499538Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1499843Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1500198Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1500555Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1501203Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1501536Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1501882Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1502243Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1502601Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1502972Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1503309Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1503616Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1503963Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1504310Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1504630Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1504948Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1505236Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1505586Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1505921Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1506248Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1506556Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1506851Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1507201Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1507546Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1507890Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1508229Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1508520Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1509118Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1509470Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1509837Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1510165Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1510461Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1510808Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1511156Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1511485Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1511805Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1512091Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1512445Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1512788Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1513120Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1513443Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1513731Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1514088Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1514430Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1514793Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1515110Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1515408Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1515752Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1516131Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1516464Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1516777Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1517072Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1517415Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1517766Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1518090Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1518436Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1518723Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1519078Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1519428Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1519751Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1520069Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1520358Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1520710Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1521084Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1521418Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1521729Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1522027Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1522418Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1522763Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1523098Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1523421Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1523728Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1524088Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1524452Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1524795Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1525115Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1525418Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1525780Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1526142Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1526469Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1526801Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1527099Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1527468Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1527861Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1528197Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1528524Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1528855Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1529231Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1529597Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1529955Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1530300Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1530608Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1530978Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1531330Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1531676Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1532005Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1532311Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1532670Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1533125Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1533479Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1533827Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1534145Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1534561Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1534927Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1535261Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1535607Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1535957Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1536339Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1536707Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1537113Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1537462Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1537764Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1538136Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1538494Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1538842Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1539173Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1539487Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.1539848Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1540212Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1540558Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1540890Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1541231Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.1541600Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1541969Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1542338Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1542711Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1543015Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.1543386Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.1543753Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.1544624Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.1544977Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.1545361Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:59:31.1545712Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:59:31.1546047Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:59:31.1546455Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:59:31.1546843Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:59:31.1547233Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:59:31.1547608Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:59:31.1547686Z 2025-03-04T20:59:31.1547997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1548509Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1548586Z 2025-03-04T20:59:31.1548917Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1550404Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1550510Z 2025-03-04T20:59:31.1550807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:59:31.1550963Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:59:31.1551031Z 2025-03-04T20:59:31.1551414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:59:31.1551660Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:59:31.1551737Z 2025-03-04T20:59:31.1552001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1552446Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1552514Z 2025-03-04T20:59:31.1552799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1554382Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1554460Z 2025-03-04T20:59:31.1554763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1554906Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:59:31.1554983Z 2025-03-04T20:59:31.1555242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1555720Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1555789Z 2025-03-04T20:59:31.1556075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1557667Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1557770Z 2025-03-04T20:59:31.1558074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1558220Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:59:31.1558294Z 2025-03-04T20:59:31.1558558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1559023Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1559090Z 2025-03-04T20:59:31.1559369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1561392Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1561469Z 2025-03-04T20:59:31.1561754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1562196Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:59:31.1562274Z 2025-03-04T20:59:31.1562545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1564191Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1564299Z 2025-03-04T20:59:31.1564587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1564747Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:59:31.1564814Z 2025-03-04T20:59:31.1565111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1565267Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:59:31.1565341Z 2025-03-04T20:59:31.1565594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1566034Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1566099Z 2025-03-04T20:59:31.1566378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1567908Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1567977Z 2025-03-04T20:59:31.1568275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1568420Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:59:31.1568494Z 2025-03-04T20:59:31.1568747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1569186Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1569253Z 2025-03-04T20:59:31.1569565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1571076Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1571177Z 2025-03-04T20:59:31.1571474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1571627Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:59:31.1571705Z 2025-03-04T20:59:31.1571974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1572440Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1572513Z 2025-03-04T20:59:31.1572800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1574493Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1574568Z 2025-03-04T20:59:31.1574876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1575042Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:59:31.1575121Z 2025-03-04T20:59:31.1575420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1575588Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:59:31.1575658Z 2025-03-04T20:59:31.1575930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1576413Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1576494Z 2025-03-04T20:59:31.1576776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1578389Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1578500Z 2025-03-04T20:59:31.1578803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1578963Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:59:31.1579032Z 2025-03-04T20:59:31.1579308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1579762Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1579841Z 2025-03-04T20:59:31.1580131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1581726Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1581807Z 2025-03-04T20:59:31.1582109Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1582265Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:59:31.1582334Z 2025-03-04T20:59:31.1582607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1583071Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1583143Z 2025-03-04T20:59:31.1583468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1585077Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1585190Z 2025-03-04T20:59:31.1585489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1585664Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:59:31.1585734Z 2025-03-04T20:59:31.1586038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1586210Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:59:31.1586282Z 2025-03-04T20:59:31.1586556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1587011Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1587088Z 2025-03-04T20:59:31.1587366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1589061Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1589147Z 2025-03-04T20:59:31.1589435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1589594Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:59:31.1589660Z 2025-03-04T20:59:31.1589926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1590422Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1590499Z 2025-03-04T20:59:31.1590765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1592345Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1592547Z 2025-03-04T20:59:31.1592834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1592986Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:59:31.1593051Z 2025-03-04T20:59:31.1593310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1593749Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1593825Z 2025-03-04T20:59:31.1594088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1595612Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1595687Z 2025-03-04T20:59:31.1595939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1596391Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:59:31.1596460Z 2025-03-04T20:59:31.1596733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1598359Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1598464Z 2025-03-04T20:59:31.1598754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1598909Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:59:31.1598982Z 2025-03-04T20:59:31.1599269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1599431Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:59:31.1599497Z 2025-03-04T20:59:31.1599757Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1600186Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1600262Z 2025-03-04T20:59:31.1600533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1602073Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1602150Z 2025-03-04T20:59:31.1602441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1602590Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:59:31.1602655Z 2025-03-04T20:59:31.1602912Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1603348Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1603424Z 2025-03-04T20:59:31.1603722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1605240Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1605347Z 2025-03-04T20:59:31.1605647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1605793Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:59:31.1605855Z 2025-03-04T20:59:31.1606108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1606536Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1606611Z 2025-03-04T20:59:31.1606877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1608413Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1608487Z 2025-03-04T20:59:31.1608771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1608940Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:59:31.1609005Z 2025-03-04T20:59:31.1609299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1609855Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:59:31.1609938Z 2025-03-04T20:59:31.1610192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1610672Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1610739Z 2025-03-04T20:59:31.1611020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1612552Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1612659Z 2025-03-04T20:59:31.1613004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1613159Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:59:31.1613240Z 2025-03-04T20:59:31.1613521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1614021Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1614092Z 2025-03-04T20:59:31.1614384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1615956Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1616024Z 2025-03-04T20:59:31.1616318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1616460Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:59:31.1616534Z 2025-03-04T20:59:31.1616786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1617230Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1617299Z 2025-03-04T20:59:31.1617604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1619125Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1619232Z 2025-03-04T20:59:31.1619519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1619676Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:59:31.1619750Z 2025-03-04T20:59:31.1620036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1620194Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:59:31.1620262Z 2025-03-04T20:59:31.1620520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1620947Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1621021Z 2025-03-04T20:59:31.1621293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1622817Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1622892Z 2025-03-04T20:59:31.1623176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1623324Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:59:31.1623390Z 2025-03-04T20:59:31.1623650Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1624125Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1624192Z 2025-03-04T20:59:31.1624474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1626005Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1626113Z 2025-03-04T20:59:31.1626407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1626557Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:59:31.1626622Z 2025-03-04T20:59:31.1626881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1627325Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1627393Z 2025-03-04T20:59:31.1627667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1629197Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1629271Z 2025-03-04T20:59:31.1629553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1629715Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:59:31.1629789Z 2025-03-04T20:59:31.1630074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1630236Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:59:31.1630302Z 2025-03-04T20:59:31.1630563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1631017Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1631091Z 2025-03-04T20:59:31.1631355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1632880Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1632990Z 2025-03-04T20:59:31.1633282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1633427Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:59:31.1633498Z 2025-03-04T20:59:31.1633756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1634200Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1634273Z 2025-03-04T20:59:31.1634540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1636089Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1636165Z 2025-03-04T20:59:31.1636456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1636598Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:59:31.1636664Z 2025-03-04T20:59:31.1636927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1637394Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1637471Z 2025-03-04T20:59:31.1637742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1639750Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1639872Z 2025-03-04T20:59:31.1640127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1640568Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:59:31.1640636Z 2025-03-04T20:59:31.1640908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1642468Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1642545Z 2025-03-04T20:59:31.1642830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1642975Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:59:31.1643050Z 2025-03-04T20:59:31.1643332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1643482Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:59:31.1643549Z 2025-03-04T20:59:31.1643805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1644218Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1644290Z 2025-03-04T20:59:31.1644587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1646115Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1646224Z 2025-03-04T20:59:31.1646513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1646659Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:59:31.1646724Z 2025-03-04T20:59:31.1646980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1647463Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1647545Z 2025-03-04T20:59:31.1647813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1649339Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1649411Z 2025-03-04T20:59:31.1649699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1649840Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:59:31.1649905Z 2025-03-04T20:59:31.1650164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1650588Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1650663Z 2025-03-04T20:59:31.1650926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1652531Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1652641Z 2025-03-04T20:59:31.1653024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1653197Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:59:31.1653267Z 2025-03-04T20:59:31.1653575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1653728Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:59:31.1653803Z 2025-03-04T20:59:31.1654068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1654521Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1654591Z 2025-03-04T20:59:31.1654884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1656493Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1656566Z 2025-03-04T20:59:31.1656879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1657021Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:59:31.1657100Z 2025-03-04T20:59:31.1657368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1657836Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1657905Z 2025-03-04T20:59:31.1658239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1659831Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1659931Z 2025-03-04T20:59:31.1660243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1660384Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:59:31.1660464Z 2025-03-04T20:59:31.1662236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1662730Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1662821Z 2025-03-04T20:59:31.1663113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1664677Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1664746Z 2025-03-04T20:59:31.1665046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1665199Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:59:31.1665272Z 2025-03-04T20:59:31.1665566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1665717Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:59:31.1665786Z 2025-03-04T20:59:31.1666056Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1666570Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1666648Z 2025-03-04T20:59:31.1666919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1668474Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1668583Z 2025-03-04T20:59:31.1668876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1669023Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:59:31.1669090Z 2025-03-04T20:59:31.1669359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1669799Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1669875Z 2025-03-04T20:59:31.1670158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1671696Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1671777Z 2025-03-04T20:59:31.1672070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1672217Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:59:31.1672285Z 2025-03-04T20:59:31.1672556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1672995Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1673071Z 2025-03-04T20:59:31.1673381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1674936Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1675043Z 2025-03-04T20:59:31.1675332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1675493Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:59:31.1675560Z 2025-03-04T20:59:31.1675867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1676009Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:59:31.1676085Z 2025-03-04T20:59:31.1676337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1676760Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1676831Z 2025-03-04T20:59:31.1677097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1678605Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1678672Z 2025-03-04T20:59:31.1678964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1679103Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:59:31.1679170Z 2025-03-04T20:59:31.1679425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1679878Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1679954Z 2025-03-04T20:59:31.1680216Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1681777Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1681876Z 2025-03-04T20:59:31.1682164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1682305Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:59:31.1682370Z 2025-03-04T20:59:31.1682628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1683061Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1683133Z 2025-03-04T20:59:31.1683401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1684931Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1685005Z 2025-03-04T20:59:31.1685284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1685438Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:59:31.1685503Z 2025-03-04T20:59:31.1685792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1685937Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:59:31.1686009Z 2025-03-04T20:59:31.1686261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1686719Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1686787Z 2025-03-04T20:59:31.1687063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1688661Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1688818Z 2025-03-04T20:59:31.1689114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1689251Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:59:31.1689329Z 2025-03-04T20:59:31.1689581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1690014Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1690079Z 2025-03-04T20:59:31.1690351Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1691925Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1692005Z 2025-03-04T20:59:31.1692319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1692462Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:59:31.1692541Z 2025-03-04T20:59:31.1692876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1693435Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1693513Z 2025-03-04T20:59:31.1693819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1695513Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1695627Z 2025-03-04T20:59:31.1695940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1696095Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:59:31.1696170Z 2025-03-04T20:59:31.1696468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1696628Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:59:31.1696698Z 2025-03-04T20:59:31.1696973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1697413Z x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1697490Z 2025-03-04T20:59:31.1697771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1699375Z x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1699455Z 2025-03-04T20:59:31.1699757Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1699909Z out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T20:59:31.1699978Z 2025-03-04T20:59:31.1700246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1700746Z x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1700827Z 2025-03-04T20:59:31.1701106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1702765Z x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1702874Z 2025-03-04T20:59:31.1703187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1703326Z out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T20:59:31.1703393Z 2025-03-04T20:59:31.1703659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1704082Z x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1704156Z 2025-03-04T20:59:31.1704417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1705936Z x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1706008Z 2025-03-04T20:59:31.1706287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1706438Z x_93 += out_51; out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T20:59:31.1706506Z 2025-03-04T20:59:31.1706799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1706939Z out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T20:59:31.1707010Z 2025-03-04T20:59:31.1707289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1707710Z x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1707776Z 2025-03-04T20:59:31.1708046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1709582Z x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1709649Z 2025-03-04T20:59:31.1709942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1710077Z out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T20:59:31.1710597Z 2025-03-04T20:59:31.1710870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1711299Z x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1711364Z 2025-03-04T20:59:31.1711636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1713149Z x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1713218Z 2025-03-04T20:59:31.1713509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1713644Z out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T20:59:31.1713720Z 2025-03-04T20:59:31.1713969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1714437Z x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1714507Z 2025-03-04T20:59:31.1714779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1716298Z x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1716404Z 2025-03-04T20:59:31.1716697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1716844Z x_99 += out_55; out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T20:59:31.1716919Z 2025-03-04T20:59:31.1717204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1717358Z out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T20:59:31.1717423Z 2025-03-04T20:59:31.1717685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1718107Z x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1718179Z 2025-03-04T20:59:31.1718456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1719998Z x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1720076Z 2025-03-04T20:59:31.1720364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1720515Z out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T20:59:31.1720580Z 2025-03-04T20:59:31.1720871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1721307Z x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1721372Z 2025-03-04T20:59:31.1721648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1723786Z x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1723864Z 2025-03-04T20:59:31.1724151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1724301Z out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T20:59:31.1724369Z 2025-03-04T20:59:31.1724632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1725068Z x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1725136Z 2025-03-04T20:59:31.1725413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1726989Z x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1727068Z 2025-03-04T20:59:31.1727353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1727522Z x_105 += out_59; out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T20:59:31.1727599Z 2025-03-04T20:59:31.1727926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1728082Z out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T20:59:31.1728149Z 2025-03-04T20:59:31.1728414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1728839Z x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1728947Z 2025-03-04T20:59:31.1729220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1730786Z x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1730864Z 2025-03-04T20:59:31.1731154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1731305Z out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T20:59:31.1731371Z 2025-03-04T20:59:31.1731637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1732062Z x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1732135Z 2025-03-04T20:59:31.1732406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1734194Z x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1734296Z 2025-03-04T20:59:31.1734618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1734781Z out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T20:59:31.1734895Z 2025-03-04T20:59:31.1735188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1735627Z x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1735703Z 2025-03-04T20:59:31.1735974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1737556Z x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1737632Z 2025-03-04T20:59:31.1737916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1738075Z x_111 += out_63; out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T20:59:31.1738141Z 2025-03-04T20:59:31.1738433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1738578Z out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T20:59:31.1738650Z 2025-03-04T20:59:31.1738897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1739326Z x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1739393Z 2025-03-04T20:59:31.1739665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1741271Z x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1741352Z 2025-03-04T20:59:31.1741736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1741878Z out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T20:59:31.1741951Z 2025-03-04T20:59:31.1742202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1742640Z x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1742740Z 2025-03-04T20:59:31.1743014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1744540Z x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1744608Z 2025-03-04T20:59:31.1744900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1745040Z out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T20:59:31.1745114Z 2025-03-04T20:59:31.1745365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1745801Z x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1745869Z 2025-03-04T20:59:31.1746144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1747681Z x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1747751Z 2025-03-04T20:59:31.1748039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1748223Z x_117 += out_67; out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T20:59:31.1748297Z 2025-03-04T20:59:31.1748582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1748735Z out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T20:59:31.1748800Z 2025-03-04T20:59:31.1749059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1749473Z x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1749608Z 2025-03-04T20:59:31.1749880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1751399Z x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1751475Z 2025-03-04T20:59:31.1751760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1751907Z out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T20:59:31.1751972Z 2025-03-04T20:59:31.1752233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1752655Z x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1752730Z 2025-03-04T20:59:31.1752998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1754529Z x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1754605Z 2025-03-04T20:59:31.1754934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1755079Z out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T20:59:31.1755143Z 2025-03-04T20:59:31.1755404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1755835Z x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1755939Z 2025-03-04T20:59:31.1756203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1757753Z x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1757833Z 2025-03-04T20:59:31.1758128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1758294Z x_123 += out_71; out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T20:59:31.1758363Z 2025-03-04T20:59:31.1758670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1758819Z out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T20:59:31.1758896Z 2025-03-04T20:59:31.1759159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1759599Z x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1759675Z 2025-03-04T20:59:31.1759954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1761629Z x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1761712Z 2025-03-04T20:59:31.1763068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1763256Z out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T20:59:31.1763337Z 2025-03-04T20:59:31.1763614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1764160Z x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1764239Z 2025-03-04T20:59:31.1764532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1766211Z x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1786399Z 2025-03-04T20:59:31.1786881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1787053Z out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T20:59:31.1787126Z 2025-03-04T20:59:31.1787424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1787893Z x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1787996Z 2025-03-04T20:59:31.1788499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1790109Z x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1790191Z 2025-03-04T20:59:31.1790668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1790841Z x_129 += out_75; out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T20:59:31.1790908Z 2025-03-04T20:59:31.1791209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1791361Z out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T20:59:31.1791505Z 2025-03-04T20:59:31.1791765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1792208Z x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1792275Z 2025-03-04T20:59:31.1792556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1794127Z x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1794197Z 2025-03-04T20:59:31.1794499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1794648Z out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T20:59:31.1794722Z 2025-03-04T20:59:31.1794990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1795449Z x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1795520Z 2025-03-04T20:59:31.1795810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1797428Z x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1797504Z 2025-03-04T20:59:31.1797807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1797958Z out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T20:59:31.1798034Z 2025-03-04T20:59:31.1798298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1798781Z x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1798854Z 2025-03-04T20:59:31.1799136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1800730Z x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1800801Z 2025-03-04T20:59:31.1801098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1801254Z x_135 += out_79; out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T20:59:31.1801330Z 2025-03-04T20:59:31.1801620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1801784Z out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T20:59:31.1801849Z 2025-03-04T20:59:31.1802108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1802528Z x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1802605Z 2025-03-04T20:59:31.1802869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1804425Z x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1804501Z 2025-03-04T20:59:31.1804784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1804931Z out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T20:59:31.1805028Z 2025-03-04T20:59:31.1805292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1805721Z x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1805796Z 2025-03-04T20:59:31.1806062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1807600Z x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1807679Z 2025-03-04T20:59:31.1807962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1808110Z out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T20:59:31.1808181Z 2025-03-04T20:59:31.1808439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1808868Z x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1808943Z 2025-03-04T20:59:31.1809217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1810768Z x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1810846Z 2025-03-04T20:59:31.1811632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1811804Z x_141 += out_83; out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T20:59:31.1811871Z 2025-03-04T20:59:31.1812224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1812368Z out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T20:59:31.1812444Z 2025-03-04T20:59:31.1812719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1813265Z x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1813353Z 2025-03-04T20:59:31.1813651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1815280Z x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1815348Z 2025-03-04T20:59:31.1815644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1815782Z out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T20:59:31.1815857Z 2025-03-04T20:59:31.1816112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1816550Z x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1816623Z 2025-03-04T20:59:31.1816887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1818455Z x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1818533Z 2025-03-04T20:59:31.1818826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1819003Z out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T20:59:31.1819069Z 2025-03-04T20:59:31.1819328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1819760Z x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1819834Z 2025-03-04T20:59:31.1820103Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1822253Z x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1822341Z 2025-03-04T20:59:31.1822626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1822784Z x_147 += out_87; out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T20:59:31.1822854Z 2025-03-04T20:59:31.1823148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1823296Z out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T20:59:31.1823371Z 2025-03-04T20:59:31.1823628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1824063Z x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1824130Z 2025-03-04T20:59:31.1824405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1825980Z x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1826117Z 2025-03-04T20:59:31.1826405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1826540Z out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T20:59:31.1826612Z 2025-03-04T20:59:31.1826865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1827302Z x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1827369Z 2025-03-04T20:59:31.1827642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1829145Z x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1829219Z 2025-03-04T20:59:31.1829513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1829650Z out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T20:59:31.1829722Z 2025-03-04T20:59:31.1829970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1830396Z x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1830461Z 2025-03-04T20:59:31.1830730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1832287Z x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1832360Z 2025-03-04T20:59:31.1832640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1832819Z x_153 += out_91; out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T20:59:31.1832891Z 2025-03-04T20:59:31.1833169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1833316Z out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T20:59:31.1833381Z 2025-03-04T20:59:31.1833635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1834057Z x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1834134Z 2025-03-04T20:59:31.1834410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1835889Z x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1835966Z 2025-03-04T20:59:31.1836243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1836389Z out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T20:59:31.1836454Z 2025-03-04T20:59:31.1836709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1837126Z x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1837202Z 2025-03-04T20:59:31.1837464Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1839035Z x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1839139Z 2025-03-04T20:59:31.1839421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1839565Z out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T20:59:31.1839630Z 2025-03-04T20:59:31.1839883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1840295Z x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1840369Z 2025-03-04T20:59:31.1840627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1842132Z x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1842209Z 2025-03-04T20:59:31.1842490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1842647Z x_159 += out_95; out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T20:59:31.1842714Z 2025-03-04T20:59:31.1843009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1843149Z out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T20:59:31.1843224Z 2025-03-04T20:59:31.1843476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1843899Z x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1843967Z 2025-03-04T20:59:31.1844240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1845810Z x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1845906Z 2025-03-04T20:59:31.1846202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1846346Z out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T20:59:31.1846425Z 2025-03-04T20:59:31.1846674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1847118Z x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1847197Z 2025-03-04T20:59:31.1847462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1848997Z x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1849072Z 2025-03-04T20:59:31.1849372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1849513Z out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T20:59:31.1849589Z 2025-03-04T20:59:31.1849840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1850282Z x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1850361Z 2025-03-04T20:59:31.1850632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1852225Z x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1852321Z 2025-03-04T20:59:31.1852614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1852772Z x_165 += out_99; out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T20:59:31.1852920Z 2025-03-04T20:59:31.1853222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1853381Z out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T20:59:31.1853460Z 2025-03-04T20:59:31.1853754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1854221Z x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1854293Z 2025-03-04T20:59:31.1854586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1856218Z x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1856302Z 2025-03-04T20:59:31.1856637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1856787Z out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T20:59:31.1856866Z 2025-03-04T20:59:31.1857131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1857595Z x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1857675Z 2025-03-04T20:59:31.1857998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1859591Z x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1859702Z 2025-03-04T20:59:31.1860012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1860157Z out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T20:59:31.1860232Z 2025-03-04T20:59:31.1860495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1860951Z x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1861024Z 2025-03-04T20:59:31.1861312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1862912Z x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1863333Z 2025-03-04T20:59:31.1863656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1863828Z x_171 += out_103; out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T20:59:31.1863907Z 2025-03-04T20:59:31.1864205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1864369Z out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T20:59:31.1864440Z 2025-03-04T20:59:31.1864718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1865214Z x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1865297Z 2025-03-04T20:59:31.1865580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1867208Z x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1867319Z 2025-03-04T20:59:31.1867623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1867779Z out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T20:59:31.1867848Z 2025-03-04T20:59:31.1868126Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1868560Z x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1868633Z 2025-03-04T20:59:31.1868899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1870412Z x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1870490Z 2025-03-04T20:59:31.1870776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1870923Z out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T20:59:31.1870988Z 2025-03-04T20:59:31.1871247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1871678Z x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1871754Z 2025-03-04T20:59:31.1872049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1873570Z x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1873673Z 2025-03-04T20:59:31.1873951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1874119Z x_177 += out_107; out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T20:59:31.1874185Z 2025-03-04T20:59:31.1874639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1874806Z out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T20:59:31.1875469Z 2025-03-04T20:59:31.1875732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1876174Z x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1876241Z 2025-03-04T20:59:31.1876520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1878066Z x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1878136Z 2025-03-04T20:59:31.1878433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1878572Z out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T20:59:31.1878650Z 2025-03-04T20:59:31.1878906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1879382Z x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1879451Z 2025-03-04T20:59:31.1879722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1881320Z x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1881425Z 2025-03-04T20:59:31.1881737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1881882Z out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T20:59:31.1881958Z 2025-03-04T20:59:31.1882226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1882687Z x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1882760Z 2025-03-04T20:59:31.1883024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1884568Z x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1884639Z 2025-03-04T20:59:31.1884940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1885105Z x_183 += out_111; out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T20:59:31.1885180Z 2025-03-04T20:59:31.1885477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1885641Z out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T20:59:31.1885711Z 2025-03-04T20:59:31.1886026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1886488Z x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.1886558Z 2025-03-04T20:59:31.1886846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1888579Z x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1888745Z 2025-03-04T20:59:31.1889067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1889230Z out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T20:59:31.1889309Z 2025-03-04T20:59:31.1889590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1890088Z x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.1890161Z 2025-03-04T20:59:31.1890477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1892126Z x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1892209Z 2025-03-04T20:59:31.1892541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1892697Z out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T20:59:31.1892780Z 2025-03-04T20:59:31.1893193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1893746Z x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.1893824Z 2025-03-04T20:59:31.1894131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.1895778Z x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.1895892Z 2025-03-04T20:59:31.1896199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.1896367Z x_189 += out_115; out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T20:59:31.1896447Z 2025-03-04T20:59:31.1896747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.1896912Z out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T20:59:31.1896981Z 2025-03-04T20:59:31.1897463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:59:31.1897628Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:59:31.1897707Z 2025-03-04T20:59:31.1898024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:31.1898183Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:59:31.1898254Z 2025-03-04T20:59:31.1898729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:59:31.1898892Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:59:31.1898969Z 2025-03-04T20:59:31.1899281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:31.1899436Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:59:31.1899516Z 2025-03-04T20:59:31.1899920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:59:31.1900114Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:59:31.1900229Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:59:31.1900392Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:59:31.1900474Z 2025-03-04T20:59:31.1900826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:59:31.1900971Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:59:31.1901041Z 2025-03-04T20:59:31.1901385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:59:31.1901534Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:59:31.1901608Z 2025-03-04T20:59:31.1901990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:59:31.1902218Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:59:31.1902287Z 2025-03-04T20:59:31.1902715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:59:31.1902843Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:59:31.1903281Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:59:31.1903416Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:59:31.1903535Z x_190: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:59:31.1903607Z 2025-03-04T20:59:31.1903909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:59:31.1904043Z tensor: "f32[82125, 4][4, 1]cpu" = x_190.to(torch.float32); x_190 = None 2025-03-04T20:59:31.1904110Z 2025-03-04T20:59:31.1904373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.1905143Z x_191: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_119 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:59:31.1905221Z 2025-03-04T20:59:31.1905496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:59:31.1905696Z x_192: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_191, inplace = False); x_191 = None 2025-03-04T20:59:31.1905762Z 2025-03-04T20:59:31.1906149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:59:31.1907025Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:59:31.1907101Z 2025-03-04T20:59:31.1907467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:59:31.1908283Z x_193: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_192 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:59:31.1908794Z 2025-03-04T20:59:31.1909145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:59:31.1909307Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:59:31.1909449Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:59:31.1909522Z 2025-03-04T20:59:31.1909943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:59:31.1910115Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75); x_193 = None 2025-03-04T20:59:31.1910290Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:59:31.1910480Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:59:31.1910546Z 2025-03-04T20:59:31.1910961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:59:31.1911171Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:59:31.1911238Z 2025-03-04T20:59:31.1911681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:59:31.1912210Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:59:31.1912386Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:59:31.1912527Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:59:31.1912602Z 2025-03-04T20:59:31.1912987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:59:31.1913169Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:59:31.1913237Z 2025-03-04T20:59:31.1913565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:59:31.1913711Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:59:31.1913785Z 2025-03-04T20:59:31.1914145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:59:31.1914288Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:59:31.1914416Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:59:31.1914568Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:59:31.1914634Z 2025-03-04T20:59:31.1914965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:59:31.1915132Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:59:31.1915262Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:59:31.1915409Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:59:31.1915486Z 2025-03-04T20:59:31.1915795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:59:31.1915924Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:59:31.1916014Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:59:31.1916145Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:59:31.1916210Z 2025-03-04T20:59:31.1916526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:59:31.1916673Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:59:31.1916772Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:59:31.1916904Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:59:31.1916976Z 2025-03-04T20:59:31.1917340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:59:31.1917503Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:59:31.1917620Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:59:31.1917693Z 2025-03-04T20:59:31.1917993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:59:31.1918158Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:59:31.1918269Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:59:31.1918342Z 2025-03-04T20:59:31.1918643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:59:31.1918803Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:59:31.1918924Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:59:31.1918990Z 2025-03-04T20:59:31.1919298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:59:31.1919484Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:59:31.1919603Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:59:31.1919668Z 2025-03-04T20:59:31.1920046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:59:31.1920192Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:59:31.1920266Z 2025-03-04T20:59:31.1920605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:59:31.1920748Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:59:31.1920813Z 2025-03-04T20:59:31.1921200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:59:31.1921343Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:59:31.1921480Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:59:31.1921634Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:59:31.1921782Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:59:31.1921850Z 2025-03-04T20:59:31.1922209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:59:31.1922349Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:59:31.1922482Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:59:31.1922633Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:59:31.1922774Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:59:31.1922842Z 2025-03-04T20:59:31.1923181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:59:31.1923301Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:59:31.1923467Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:59:31.1923599Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:59:31.1923672Z 2025-03-04T20:59:31.1924007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:59:31.1924134Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:59:31.1924301Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:59:31.1924440Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:59:31.1924506Z 2025-03-04T20:59:31.1924825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:59:31.1924924Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:59:31.1925051Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:59:31.1925116Z 2025-03-04T20:59:31.1925433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:59:31.1925526Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:59:31.1925647Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:59:31.1925746Z 2025-03-04T20:59:31.1926063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:59:31.1926176Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:59:31.1926311Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:59:31.1926376Z 2025-03-04T20:59:31.1926686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:59:31.1926831Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:59:31.1926962Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:59:31.1927029Z 2025-03-04T20:59:31.1927384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:59:31.1927575Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:59:31.1927641Z 2025-03-04T20:59:31.1927987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:59:31.1928147Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:59:31.1928222Z 2025-03-04T20:59:31.1928603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:59:31.1928783Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:59:31.1928851Z 2025-03-04T20:59:31.1929343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:59:31.1929479Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:59:31.1929551Z 2025-03-04T20:59:31.1929850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:31.1930003Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:59:31.1930068Z 2025-03-04T20:59:31.1930520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:59:31.1930637Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:59:31.1930750Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:59:31.1930867Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:59:31.1930940Z 2025-03-04T20:59:31.1931407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:59:31.1931583Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:59:31.1931819Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:59:31.1931895Z 2025-03-04T20:59:31.1932393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:59:31.1932574Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:59:31.1932640Z 2025-03-04T20:59:31.1933037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:31.1933206Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:59:31.1933357Z 2025-03-04T20:59:31.1933763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:59:31.1933933Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:59:31.1934003Z 2025-03-04T20:59:31.1934339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:59:31.1934496Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:59:31.1934565Z 2025-03-04T20:59:31.1934960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:59:31.1935108Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:59:31.1935184Z 2025-03-04T20:59:31.1935687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:59:31.1935842Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:59:31.1935970Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:59:31.1936149Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:59:31.1936288Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:59:31.1936364Z 2025-03-04T20:59:31.1936755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:59:31.1936892Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:59:31.1936960Z 2025-03-04T20:59:31.1936972Z 2025-03-04T20:59:31.1937079Z class GraphModule(torch.nn.Module): 2025-03-04T20:59:31.2034043Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: 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L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", 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1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:59:31.2129234Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:59:31.2129797Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2130681Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2131574Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2132451Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2133402Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2134265Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2135157Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2136286Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2137195Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2138078Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2138888Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2139802Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2140712Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2141535Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2142317Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2143064Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2143847Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2144682Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2145497Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2146297Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2147052Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:59:31.2147864Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2148736Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2149636Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2150447Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2151213Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2151969Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2152823Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2153626Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2154397Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2155119Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2155913Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2156737Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2157532Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2158287Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2159014Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2159771Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2160570Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2161362Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2162110Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2162832Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2163579Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2164381Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2165131Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2166568Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2167332Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2168133Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2168907Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2169640Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2170350Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2171050Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2171779Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2172567Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2173420Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2174227Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2174963Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2175704Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2176484Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2177241Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2177977Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2178675Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2179405Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2180195Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2180951Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2181675Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2182351Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2183122Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2183910Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2184666Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2185471Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2186188Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:59:31.2186944Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2187752Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2188700Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2189483Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2190206Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2190928Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2191682Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2192403Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2193104Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2193774Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2194470Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2195214Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2195936Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2196643Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2197376Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2198078Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2198818Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2199534Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2200274Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2200940Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2201632Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2202379Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2203102Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2203798Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2204459Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2205149Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2205893Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2206621Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2207324Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2207987Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2208680Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2209424Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2210145Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2210923Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2211644Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2212397Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2213292Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2214628Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2215401Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2216077Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2216790Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2217544Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2218283Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2218996Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2219670Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2220374Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2222086Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2223431Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2224162Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2224845Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2225563Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2226335Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2227054Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2227804Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2228474Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2229170Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2229915Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2230677Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2231380Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2232048Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2232745Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2233495Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2234219Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2234920Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2235603Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:59:31.2236326Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2237103Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2237865Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2238595Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2239279Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2239977Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2240729Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2241494Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2242190Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2242847Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2243539Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2244314Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2245034Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2245737Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2246403Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2247101Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2247851Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2248576Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2249276Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2249939Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2250660Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2251428Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2252171Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2252998Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2253757Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2254521Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2255328Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2256144Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2256883Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2257602Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2258346Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2259152Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2259906Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2260635Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2261330Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2262059Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2262842Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2263592Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2264304Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2264988Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2265721Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2266852Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2267629Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2268365Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2269056Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2269793Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2270619Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2271385Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2272117Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2272816Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2273590Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2274378Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2275132Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2275864Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2276560Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2277288Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2278073Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2278827Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2279560Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2280254Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2280985Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2281767Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2282514Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2283223Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2283900Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2284611Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2285404Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2286150Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2286871Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2287589Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2288439Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2289236Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2290005Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2290749Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2291498Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2292243Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2293138Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2293914Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2294711Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2295465Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2296251Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2297092Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2297908Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2298694Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2299446Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2300344Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2301179Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2302442Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2303274Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2304108Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2304894Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2305743Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2306553Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2307337Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2308083Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2308868Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2309701Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2310463Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2311200Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2311898Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2312633Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2313422Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2314189Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2315385Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2316121Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2316845Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2317615Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2318359Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2319108Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2319784Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2320494Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2321246Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2321977Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2322688Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2323358Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2324064Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2324816Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2325550Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2326259Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2326932Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2327635Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2328390Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2329114Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2329821Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2330531Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2331248Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2332024Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2332764Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2333632Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2334374Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2335146Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2335925Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2336680Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2337411Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2338107Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2338836Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2339619Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2340996Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2341746Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2342467Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2343210Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2344007Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2344778Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2345562Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2346267Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2347002Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2347787Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2348593Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2349348Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2350059Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2350801Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2351603Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2352380Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2353124Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2353844Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2354564Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2355340Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2356094Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2356822Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2357511Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2358242Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2359016Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2359799Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2360513Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2361188Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2361899Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2362707Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2363449Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2364165Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2364848Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2365561Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2366327Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2367070Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2368174Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2368863Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2369573Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2370337Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2371086Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2371802Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2372489Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2373357Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2374196Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2375105Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2375891Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2376637Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2377418Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2378293Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2379108Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2379895Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2380651Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2381372Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2382152Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2382900Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2383626Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2384310Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2385032Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2385804Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2386551Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2387272Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2387956Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2388793Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2389667Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2390436Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2391171Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2391864Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2392638Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2393444Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2394186Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2395481Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2396167Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2396885Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2397652Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2398392Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2399106Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2399820Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2400534Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2401301Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2402038Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2402765Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2403464Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2404267Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2405058Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2405822Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2406557Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2407289Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2408032Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2408819Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2409578Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2410313Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2411010Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2411744Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2412525Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2413387Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2414165Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2414908Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2415956Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2416755Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2417522Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2418257Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2418958Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2419732Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2420529Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2421295Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2422054Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2422730Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2423446Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2424230Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2424970Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2425680Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2426366Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2427074Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2427835Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2428578Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2429299Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2429984Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2430696Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2431455Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2432195Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2432914Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2433625Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2434341Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2435105Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2435842Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2436573Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2437245Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2437951Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2438734Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2439487Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2440221Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2440884Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2441583Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2442330Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2443062Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2443780Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2444463Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2445180Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2445951Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2446703Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2447998Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2448702Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2449418Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2450195Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2450978Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2451700Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2452391Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2453263Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2454108Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2454902Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2455636Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2456318Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2457037Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2457807Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2458551Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2459272Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2459952Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2460665Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2461431Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2462216Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2462938Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2463622Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2464332Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2465200Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2465946Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2466658Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2467340Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2469285Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2470163Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2470901Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2471606Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2472285Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2473009Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2473759Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2474488Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2475190Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2475871Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2476585Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2477334Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2478142Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2478865Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2479554Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T20:59:31.2480280Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2481082Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2481809Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2482505Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2483186Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T20:59:31.2483900Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2484668Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2485411Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2486130Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2486820Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T20:59:31.2487527Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:59:31.2488405Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:59:31.2489168Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:59:31.2489908Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:59:31.2490682Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:59:31.2491630Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:59:31.2492490Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:59:31.2494219Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:59:31.2495074Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:59:31.2495870Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:59:31.2496725Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:59:31.2497227Z 2025-03-04T20:59:31.2497653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2498503Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2499152Z 2025-03-04T20:59:31.2499544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2501430Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2503601Z 2025-03-04T20:59:31.2504011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:59:31.2504531Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:59:31.2504817Z 2025-03-04T20:59:31.2505559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:59:31.2506274Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:59:31.2506654Z 2025-03-04T20:59:31.2507022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2507781Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2508326Z 2025-03-04T20:59:31.2508702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2513512Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2515554Z 2025-03-04T20:59:31.2515972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2516924Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:59:31.2517196Z 2025-03-04T20:59:31.2517548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2517988Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2518071Z 2025-03-04T20:59:31.2518361Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2521144Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2521261Z 2025-03-04T20:59:31.2522586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2522765Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:59:31.2522840Z 2025-03-04T20:59:31.2523135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2523612Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2523692Z 2025-03-04T20:59:31.2523972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2525573Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2525686Z 2025-03-04T20:59:31.2525948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2526409Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:59:31.2526477Z 2025-03-04T20:59:31.2526758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2528461Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2528534Z 2025-03-04T20:59:31.2528851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2529013Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:59:31.2529094Z 2025-03-04T20:59:31.2529399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2529570Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:59:31.2529643Z 2025-03-04T20:59:31.2529923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2530379Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2530458Z 2025-03-04T20:59:31.2530743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2532938Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2533040Z 2025-03-04T20:59:31.2534679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2534851Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:59:31.2534921Z 2025-03-04T20:59:31.2535212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2535674Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2535754Z 2025-03-04T20:59:31.2536048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2537676Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2537755Z 2025-03-04T20:59:31.2538066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2538229Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:59:31.2538299Z 2025-03-04T20:59:31.2538582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2539046Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2539121Z 2025-03-04T20:59:31.2539411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2552792Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2552956Z 2025-03-04T20:59:31.2553293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2553528Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:59:31.2553596Z 2025-03-04T20:59:31.2553905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2554064Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:59:31.2554141Z 2025-03-04T20:59:31.2554409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2554864Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2554944Z 2025-03-04T20:59:31.2555222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2556809Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2556879Z 2025-03-04T20:59:31.2557181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2557338Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:59:31.2557410Z 2025-03-04T20:59:31.2557678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2558120Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2558193Z 2025-03-04T20:59:31.2558470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2560039Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2560114Z 2025-03-04T20:59:31.2560431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2560578Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:59:31.2560645Z 2025-03-04T20:59:31.2560911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2561353Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2561426Z 2025-03-04T20:59:31.2561694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2563236Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2563311Z 2025-03-04T20:59:31.2563594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2563764Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:59:31.2563830Z 2025-03-04T20:59:31.2564127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2564283Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:59:31.2564355Z 2025-03-04T20:59:31.2564611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2565048Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2565117Z 2025-03-04T20:59:31.2565393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2566934Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2567039Z 2025-03-04T20:59:31.2567335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2567486Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:59:31.2567560Z 2025-03-04T20:59:31.2567828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2568326Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2568399Z 2025-03-04T20:59:31.2568691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2574972Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2575092Z 2025-03-04T20:59:31.2575428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2575587Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:59:31.2575667Z 2025-03-04T20:59:31.2575941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2576421Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2576491Z 2025-03-04T20:59:31.2576790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2578534Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2578662Z 2025-03-04T20:59:31.2578943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2579418Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:59:31.2579499Z 2025-03-04T20:59:31.2579847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2581540Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2581620Z 2025-03-04T20:59:31.2581919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2582085Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:59:31.2582157Z 2025-03-04T20:59:31.2582468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2582632Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:59:31.2582710Z 2025-03-04T20:59:31.2582981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2583439Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2583508Z 2025-03-04T20:59:31.2583797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2585477Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2585548Z 2025-03-04T20:59:31.2585864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2586053Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:59:31.2586130Z 2025-03-04T20:59:31.2586401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2586840Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2586905Z 2025-03-04T20:59:31.2587175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2588839Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2588914Z 2025-03-04T20:59:31.2589215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2589371Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:59:31.2589448Z 2025-03-04T20:59:31.2589714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2590173Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2590240Z 2025-03-04T20:59:31.2590515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2592147Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2592221Z 2025-03-04T20:59:31.2592513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2592677Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:59:31.2592832Z 2025-03-04T20:59:31.2593113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2593278Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:59:31.2593344Z 2025-03-04T20:59:31.2593603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2594033Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2594106Z 2025-03-04T20:59:31.2594374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2595901Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2595980Z 2025-03-04T20:59:31.2596264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2596414Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:59:31.2596480Z 2025-03-04T20:59:31.2596741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2597175Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2597249Z 2025-03-04T20:59:31.2597521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2599075Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2599152Z 2025-03-04T20:59:31.2599469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2599621Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:59:31.2599687Z 2025-03-04T20:59:31.2599950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2600395Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2600460Z 2025-03-04T20:59:31.2600736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2602284Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2602359Z 2025-03-04T20:59:31.2602641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2602809Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:59:31.2602876Z 2025-03-04T20:59:31.2603172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2603330Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:59:31.2603395Z 2025-03-04T20:59:31.2603655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2604081Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2604158Z 2025-03-04T20:59:31.2604425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2605999Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2606103Z 2025-03-04T20:59:31.2606388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2606537Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:59:31.2606602Z 2025-03-04T20:59:31.2606858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2607285Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2607363Z 2025-03-04T20:59:31.2607626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2609152Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2609228Z 2025-03-04T20:59:31.2609512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2609660Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:59:31.2609724Z 2025-03-04T20:59:31.2609979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2610415Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2610488Z 2025-03-04T20:59:31.2610753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2612306Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2612409Z 2025-03-04T20:59:31.2612723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2613024Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:59:31.2613108Z 2025-03-04T20:59:31.2613446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2613618Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:59:31.2613701Z 2025-03-04T20:59:31.2614001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2614457Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2614531Z 2025-03-04T20:59:31.2614848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2616610Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2616695Z 2025-03-04T20:59:31.2617926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2618145Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:59:31.2618234Z 2025-03-04T20:59:31.2618545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2619051Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2619132Z 2025-03-04T20:59:31.2619450Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2621174Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2621287Z 2025-03-04T20:59:31.2621587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2621731Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:59:31.2621803Z 2025-03-04T20:59:31.2622056Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2622493Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2622562Z 2025-03-04T20:59:31.2622839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2624458Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2624532Z 2025-03-04T20:59:31.2624808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2625278Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:59:31.2625356Z 2025-03-04T20:59:31.2625638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2627345Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2627428Z 2025-03-04T20:59:31.2627732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2627896Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:59:31.2627997Z 2025-03-04T20:59:31.2628305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2628456Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:59:31.2628535Z 2025-03-04T20:59:31.2628799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2629247Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2629317Z 2025-03-04T20:59:31.2629606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2631197Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2631269Z 2025-03-04T20:59:31.2631577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2631718Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:59:31.2631795Z 2025-03-04T20:59:31.2632062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2632513Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2632582Z 2025-03-04T20:59:31.2632875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2634420Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2634486Z 2025-03-04T20:59:31.2634779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2634955Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:59:31.2635029Z 2025-03-04T20:59:31.2635283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2635710Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2635776Z 2025-03-04T20:59:31.2636053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2637609Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2637677Z 2025-03-04T20:59:31.2637960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2638115Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:59:31.2638191Z 2025-03-04T20:59:31.2638487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2638648Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:59:31.2638718Z 2025-03-04T20:59:31.2638992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2639428Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2639506Z 2025-03-04T20:59:31.2639797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2642299Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2642426Z 2025-03-04T20:59:31.2642759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2642920Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:59:31.2642996Z 2025-03-04T20:59:31.2643280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2643741Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2643821Z 2025-03-04T20:59:31.2644112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2645729Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2645809Z 2025-03-04T20:59:31.2646113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2646265Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:59:31.2646333Z 2025-03-04T20:59:31.2646614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2647071Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2647146Z 2025-03-04T20:59:31.2647436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2649088Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2649171Z 2025-03-04T20:59:31.2649471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2649671Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:59:31.2649741Z 2025-03-04T20:59:31.2650052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2650204Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:59:31.2650281Z 2025-03-04T20:59:31.2650550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2651000Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2651088Z 2025-03-04T20:59:31.2651358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2652965Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2653043Z 2025-03-04T20:59:31.2653337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2653494Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:59:31.2653570Z 2025-03-04T20:59:31.2653861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2654358Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2654440Z 2025-03-04T20:59:31.2654749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2656443Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2656548Z 2025-03-04T20:59:31.2656859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2657010Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:59:31.2657082Z 2025-03-04T20:59:31.2657354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2657806Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2657882Z 2025-03-04T20:59:31.2658163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2659826Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2659914Z 2025-03-04T20:59:31.2660218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2660380Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:59:31.2660451Z 2025-03-04T20:59:31.2660763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2660914Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:59:31.2660993Z 2025-03-04T20:59:31.2661259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2661703Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2661772Z 2025-03-04T20:59:31.2662046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2663615Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2663748Z 2025-03-04T20:59:31.2664053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2664189Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:59:31.2664265Z 2025-03-04T20:59:31.2664529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2664980Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2665051Z 2025-03-04T20:59:31.2665336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2666910Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2666988Z 2025-03-04T20:59:31.2667283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2667427Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:59:31.2667502Z 2025-03-04T20:59:31.2667767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2668224Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2668294Z 2025-03-04T20:59:31.2668586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2670855Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2670995Z 2025-03-04T20:59:31.2671352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2671519Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:59:31.2671639Z 2025-03-04T20:59:31.2671956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2672112Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:59:31.2672181Z 2025-03-04T20:59:31.2672447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2672881Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2672960Z 2025-03-04T20:59:31.2673236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2674833Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2674912Z 2025-03-04T20:59:31.2675207Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2675353Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:59:31.2675421Z 2025-03-04T20:59:31.2675690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2676135Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2676214Z 2025-03-04T20:59:31.2676491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2678154Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2678262Z 2025-03-04T20:59:31.2678572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2678721Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:59:31.2678791Z 2025-03-04T20:59:31.2679067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2679518Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2679597Z 2025-03-04T20:59:31.2679879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2681485Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2681562Z 2025-03-04T20:59:31.2681861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2682026Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:59:31.2682094Z 2025-03-04T20:59:31.2682405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2682554Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:59:31.2682641Z 2025-03-04T20:59:31.2682895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2683325Z x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2683391Z 2025-03-04T20:59:31.2683698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2685208Z x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2685307Z 2025-03-04T20:59:31.2685600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2685736Z out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T20:59:31.2685810Z 2025-03-04T20:59:31.2686065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2686501Z x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2686568Z 2025-03-04T20:59:31.2686848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2688501Z x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2688584Z 2025-03-04T20:59:31.2688901Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2689046Z out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T20:59:31.2689125Z 2025-03-04T20:59:31.2689392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2689851Z x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2689923Z 2025-03-04T20:59:31.2690215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2691936Z x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2692052Z 2025-03-04T20:59:31.2692362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2692519Z x_93 += out_51; out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T20:59:31.2692631Z 2025-03-04T20:59:31.2693010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2694329Z out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T20:59:31.2694474Z 2025-03-04T20:59:31.2694855Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2695319Z x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2695400Z 2025-03-04T20:59:31.2695676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2697233Z x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2697319Z 2025-03-04T20:59:31.2697620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2697774Z out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T20:59:31.2697845Z 2025-03-04T20:59:31.2698124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2698575Z x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2698652Z 2025-03-04T20:59:31.2699004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2700606Z x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2700734Z 2025-03-04T20:59:31.2701037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2701188Z out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T20:59:31.2701257Z 2025-03-04T20:59:31.2701530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2701987Z x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2702059Z 2025-03-04T20:59:31.2702350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2703957Z x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2704037Z 2025-03-04T20:59:31.2704336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2704498Z x_99 += out_55; out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T20:59:31.2704568Z 2025-03-04T20:59:31.2704876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2705036Z out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T20:59:31.2705106Z 2025-03-04T20:59:31.2705381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2705857Z x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2705937Z 2025-03-04T20:59:31.2706218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2707845Z x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2707992Z 2025-03-04T20:59:31.2708300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2708458Z out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T20:59:31.2708528Z 2025-03-04T20:59:31.2708799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2709250Z x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2709329Z 2025-03-04T20:59:31.2709609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2711166Z x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2711240Z 2025-03-04T20:59:31.2711526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2711674Z out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T20:59:31.2711739Z 2025-03-04T20:59:31.2711998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2712430Z x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2712503Z 2025-03-04T20:59:31.2712808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2714351Z x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2714462Z 2025-03-04T20:59:31.2714742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2714906Z x_105 += out_59; out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T20:59:31.2714973Z 2025-03-04T20:59:31.2715264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2715404Z out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T20:59:31.2715478Z 2025-03-04T20:59:31.2715730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2716160Z x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2716230Z 2025-03-04T20:59:31.2716515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2719177Z x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2719276Z 2025-03-04T20:59:31.2719615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2719777Z out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T20:59:31.2719857Z 2025-03-04T20:59:31.2720115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2720615Z x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2720686Z 2025-03-04T20:59:31.2720964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2722481Z x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2722597Z 2025-03-04T20:59:31.2722907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2723055Z out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T20:59:31.2723131Z 2025-03-04T20:59:31.2723396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2723886Z x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2723956Z 2025-03-04T20:59:31.2724244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2725881Z x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2725955Z 2025-03-04T20:59:31.2726258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2726419Z x_111 += out_63; out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T20:59:31.2726494Z 2025-03-04T20:59:31.2726796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2726954Z out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T20:59:31.2727023Z 2025-03-04T20:59:31.2727295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2727778Z x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2727858Z 2025-03-04T20:59:31.2728140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2729772Z x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2729884Z 2025-03-04T20:59:31.2730191Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2730348Z out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T20:59:31.2730418Z 2025-03-04T20:59:31.2730693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2731147Z x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2731225Z 2025-03-04T20:59:31.2731507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2733307Z x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2733403Z 2025-03-04T20:59:31.2733733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2733897Z out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T20:59:31.2733974Z 2025-03-04T20:59:31.2734276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2734788Z x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2734868Z 2025-03-04T20:59:31.2735144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2736760Z x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2736877Z 2025-03-04T20:59:31.2737174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2737343Z x_117 += out_67; out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T20:59:31.2737413Z 2025-03-04T20:59:31.2737724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2737876Z out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T20:59:31.2737953Z 2025-03-04T20:59:31.2738221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2738671Z x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2738741Z 2025-03-04T20:59:31.2739027Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2740701Z x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2740780Z 2025-03-04T20:59:31.2741090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2741239Z out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T20:59:31.2741317Z 2025-03-04T20:59:31.2741619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2742075Z x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2742154Z 2025-03-04T20:59:31.2742434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2744060Z x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2744172Z 2025-03-04T20:59:31.2744493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2744641Z out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T20:59:31.2744719Z 2025-03-04T20:59:31.2744985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2745448Z x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2745535Z 2025-03-04T20:59:31.2745799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2747331Z x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2747399Z 2025-03-04T20:59:31.2747688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2747847Z x_123 += out_71; out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T20:59:31.2747915Z 2025-03-04T20:59:31.2748211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2748383Z out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T20:59:31.2748460Z 2025-03-04T20:59:31.2748717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2749142Z x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2749209Z 2025-03-04T20:59:31.2749517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2751032Z x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2751107Z 2025-03-04T20:59:31.2751399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2751535Z out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T20:59:31.2751613Z 2025-03-04T20:59:31.2751863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2752292Z x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2752359Z 2025-03-04T20:59:31.2752643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2754260Z x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2754338Z 2025-03-04T20:59:31.2754647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2754789Z out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T20:59:31.2754866Z 2025-03-04T20:59:31.2755166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2755630Z x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2755700Z 2025-03-04T20:59:31.2755988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2757633Z x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2757709Z 2025-03-04T20:59:31.2758011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2758173Z x_129 += out_75; out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T20:59:31.2758249Z 2025-03-04T20:59:31.2758552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2758707Z out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T20:59:31.2758776Z 2025-03-04T20:59:31.2759050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2759494Z x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2759572Z 2025-03-04T20:59:31.2759852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2761483Z x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2761561Z 2025-03-04T20:59:31.2761863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2762049Z out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T20:59:31.2762120Z 2025-03-04T20:59:31.2762404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2762858Z x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2762968Z 2025-03-04T20:59:31.2763252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2764916Z x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2764993Z 2025-03-04T20:59:31.2765296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2765454Z out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T20:59:31.2765522Z 2025-03-04T20:59:31.2765799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2766253Z x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2766328Z 2025-03-04T20:59:31.2766611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2768223Z x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2768302Z 2025-03-04T20:59:31.2768601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2768766Z x_135 += out_79; out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T20:59:31.2768872Z 2025-03-04T20:59:31.2769183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2769335Z out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T20:59:31.2769412Z 2025-03-04T20:59:31.2769693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2770173Z x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2770291Z 2025-03-04T20:59:31.2770603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2773296Z x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2773399Z 2025-03-04T20:59:31.2773761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2773926Z out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T20:59:31.2774012Z 2025-03-04T20:59:31.2774308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2774804Z x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2774879Z 2025-03-04T20:59:31.2775173Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2776813Z x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2776887Z 2025-03-04T20:59:31.2777264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2777414Z out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T20:59:31.2777493Z 2025-03-04T20:59:31.2777761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2778228Z x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2778331Z 2025-03-04T20:59:31.2778622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2780301Z x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2780381Z 2025-03-04T20:59:31.2780688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2780851Z x_141 += out_83; out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T20:59:31.2780928Z 2025-03-04T20:59:31.2781231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2781392Z out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T20:59:31.2781464Z 2025-03-04T20:59:31.2782776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2783280Z x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2783359Z 2025-03-04T20:59:31.2783661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2785573Z x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2785664Z 2025-03-04T20:59:31.2785972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2786127Z out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T20:59:31.2786196Z 2025-03-04T20:59:31.2786470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2786925Z x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2787033Z 2025-03-04T20:59:31.2787329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2788994Z x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2789079Z 2025-03-04T20:59:31.2789369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2789513Z out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T20:59:31.2789589Z 2025-03-04T20:59:31.2789844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2790282Z x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2790350Z 2025-03-04T20:59:31.2790627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2792209Z x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2792293Z 2025-03-04T20:59:31.2792714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2792876Z x_147 += out_87; out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T20:59:31.2792952Z 2025-03-04T20:59:31.2793257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2793413Z out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T20:59:31.2793483Z 2025-03-04T20:59:31.2793807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2794248Z x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2794327Z 2025-03-04T20:59:31.2794607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2796205Z x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2796285Z 2025-03-04T20:59:31.2796565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2796711Z out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T20:59:31.2796777Z 2025-03-04T20:59:31.2797031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2797456Z x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2797527Z 2025-03-04T20:59:31.2797791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2799351Z x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2799427Z 2025-03-04T20:59:31.2799714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2799856Z out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T20:59:31.2799922Z 2025-03-04T20:59:31.2800181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2800641Z x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2800715Z 2025-03-04T20:59:31.2800984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2802508Z x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2802584Z 2025-03-04T20:59:31.2802864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2803019Z x_153 += out_91; out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T20:59:31.2803086Z 2025-03-04T20:59:31.2803378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2803522Z out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T20:59:31.2803597Z 2025-03-04T20:59:31.2803850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2804280Z x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2804348Z 2025-03-04T20:59:31.2804685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2806257Z x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2806331Z 2025-03-04T20:59:31.2806622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2806759Z out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T20:59:31.2806866Z 2025-03-04T20:59:31.2807118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2807558Z x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2807627Z 2025-03-04T20:59:31.2807915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2809525Z x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2809597Z 2025-03-04T20:59:31.2809904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2810047Z out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T20:59:31.2810124Z 2025-03-04T20:59:31.2810394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2810853Z x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2810923Z 2025-03-04T20:59:31.2811211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2812963Z x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2813051Z 2025-03-04T20:59:31.2813403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2813572Z x_159 += out_95; out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T20:59:31.2813655Z 2025-03-04T20:59:31.2813973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2814177Z out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T20:59:31.2814251Z 2025-03-04T20:59:31.2814544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2814989Z x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2815068Z 2025-03-04T20:59:31.2815349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2816957Z x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2817040Z 2025-03-04T20:59:31.2817340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2817503Z out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T20:59:31.2817572Z 2025-03-04T20:59:31.2817846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2818306Z x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2818384Z 2025-03-04T20:59:31.2818663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2821249Z x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2821349Z 2025-03-04T20:59:31.2821684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2821847Z out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T20:59:31.2821946Z 2025-03-04T20:59:31.2822216Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2822665Z x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2822741Z 2025-03-04T20:59:31.2823019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2824556Z x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2824632Z 2025-03-04T20:59:31.2824913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2825077Z x_165 += out_99; out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T20:59:31.2825146Z 2025-03-04T20:59:31.2825441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2825595Z out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T20:59:31.2825672Z 2025-03-04T20:59:31.2825928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2826361Z x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2826434Z 2025-03-04T20:59:31.2826703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2828327Z x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2828397Z 2025-03-04T20:59:31.2828726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2828875Z out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T20:59:31.2828941Z 2025-03-04T20:59:31.2829206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2829644Z x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2829717Z 2025-03-04T20:59:31.2829987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2831550Z x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2831624Z 2025-03-04T20:59:31.2831916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2832066Z out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T20:59:31.2832134Z 2025-03-04T20:59:31.2832398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2832837Z x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2832912Z 2025-03-04T20:59:31.2833182Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2834781Z x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2834862Z 2025-03-04T20:59:31.2835152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2835403Z x_171 += out_103; out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T20:59:31.2835471Z 2025-03-04T20:59:31.2835765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2835917Z out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T20:59:31.2835989Z 2025-03-04T20:59:31.2836243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2836680Z x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2836747Z 2025-03-04T20:59:31.2837020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2838558Z x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2838634Z 2025-03-04T20:59:31.2838932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2839083Z out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T20:59:31.2839161Z 2025-03-04T20:59:31.2839430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2839896Z x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2839967Z 2025-03-04T20:59:31.2840263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2841876Z x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2841981Z 2025-03-04T20:59:31.2842274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2842414Z out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T20:59:31.2842488Z 2025-03-04T20:59:31.2842739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2843180Z x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2843245Z 2025-03-04T20:59:31.2843514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2845043Z x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2845108Z 2025-03-04T20:59:31.2845398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2845556Z x_177 += out_107; out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T20:59:31.2845628Z 2025-03-04T20:59:31.2845916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2846069Z out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T20:59:31.2846134Z 2025-03-04T20:59:31.2846391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2846812Z x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2846887Z 2025-03-04T20:59:31.2847151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2848705Z x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2848816Z 2025-03-04T20:59:31.2849104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2849251Z out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T20:59:31.2849317Z 2025-03-04T20:59:31.2849573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2850009Z x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2850087Z 2025-03-04T20:59:31.2850366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2851990Z x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2862151Z 2025-03-04T20:59:31.2862732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2863015Z out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T20:59:31.2863109Z 2025-03-04T20:59:31.2863587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2864081Z x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2864160Z 2025-03-04T20:59:31.2864429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2866112Z x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2866203Z 2025-03-04T20:59:31.2866496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2866663Z x_183 += out_111; out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T20:59:31.2866737Z 2025-03-04T20:59:31.2867023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2867180Z out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T20:59:31.2867248Z 2025-03-04T20:59:31.2867510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2867941Z x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T20:59:31.2868020Z 2025-03-04T20:59:31.2868291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2869841Z x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2869985Z 2025-03-04T20:59:31.2870276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2870423Z out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T20:59:31.2870489Z 2025-03-04T20:59:31.2870747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2871183Z x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T20:59:31.2871259Z 2025-03-04T20:59:31.2871562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2873487Z x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2873597Z 2025-03-04T20:59:31.2873889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2874037Z out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T20:59:31.2874103Z 2025-03-04T20:59:31.2874364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2874795Z x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T20:59:31.2874869Z 2025-03-04T20:59:31.2875135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:59:31.2877159Z x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:59:31.2877283Z 2025-03-04T20:59:31.2877569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:59:31.2877742Z x_189 += out_115; out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T20:59:31.2877809Z 2025-03-04T20:59:31.2878102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:59:31.2878249Z out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T20:59:31.2878324Z 2025-03-04T20:59:31.2878777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:59:31.2878951Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:59:31.2879052Z 2025-03-04T20:59:31.2879376Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:31.2879523Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:59:31.2879589Z 2025-03-04T20:59:31.2880032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:59:31.2880186Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:59:31.2880276Z 2025-03-04T20:59:31.2880569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:31.2880720Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:59:31.2880788Z 2025-03-04T20:59:31.2881193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:59:31.2881386Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:59:31.2881499Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:59:31.2881629Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:59:31.2881708Z 2025-03-04T20:59:31.2882061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:59:31.2882207Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:59:31.2882276Z 2025-03-04T20:59:31.2882633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:59:31.2882760Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:59:31.2882834Z 2025-03-04T20:59:31.2883238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:59:31.2883475Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:59:31.2883545Z 2025-03-04T20:59:31.2884003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:59:31.2884162Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:59:31.2884627Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:59:31.2884758Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:59:31.2884890Z x_190: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:59:31.2884959Z 2025-03-04T20:59:31.2885290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:59:31.2885425Z tensor: "f32[82125, 4][4, 1]cpu" = x_190.to(torch.float32); x_190 = None 2025-03-04T20:59:31.2885506Z 2025-03-04T20:59:31.2885821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:31.2886652Z x_191: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_119 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:59:31.2886730Z 2025-03-04T20:59:31.2887036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:59:31.2887243Z x_192: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_191, inplace = False); x_191 = None 2025-03-04T20:59:31.2887315Z 2025-03-04T20:59:31.2887730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:59:31.2888811Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:59:31.2888900Z 2025-03-04T20:59:31.2889289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:59:31.2890217Z x_193: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_192 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:59:31.2890295Z 2025-03-04T20:59:31.2890669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:59:31.2890854Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:59:31.2891091Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:59:31.2891173Z 2025-03-04T20:59:31.2891652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:59:31.2891836Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75); x_193 = None 2025-03-04T20:59:31.2892033Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:59:31.2892237Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:59:31.2892310Z 2025-03-04T20:59:31.2892762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:59:31.2893182Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:59:31.2893272Z 2025-03-04T20:59:31.2893743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:59:31.2893922Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:59:31.2894092Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:59:31.2894301Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:59:31.2894371Z 2025-03-04T20:59:31.2894771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:59:31.2894957Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:59:31.2895034Z 2025-03-04T20:59:31.2895369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:59:31.2895525Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:59:31.2895596Z 2025-03-04T20:59:31.2895934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:59:31.2896080Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:59:31.2896215Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:59:31.2896375Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:59:31.2896448Z 2025-03-04T20:59:31.2896789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:59:31.2896919Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:59:31.2897053Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:59:31.2897208Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:59:31.2897286Z 2025-03-04T20:59:31.2897617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:59:31.2897751Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:59:31.2897868Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:59:31.2898010Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:59:31.2898080Z 2025-03-04T20:59:31.2898418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:59:31.2898570Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:59:31.2898672Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:59:31.2898867Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:59:31.2898952Z 2025-03-04T20:59:31.2899341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:59:31.2899514Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:59:31.2899671Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:59:31.2900697Z 2025-03-04T20:59:31.2901108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:59:31.2901288Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:59:31.2901414Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:59:31.2901496Z 2025-03-04T20:59:31.2901850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:59:31.2902078Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:59:31.2902200Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:59:31.2902280Z 2025-03-04T20:59:31.2902607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:59:31.2902808Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:59:31.2902926Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:59:31.2903005Z 2025-03-04T20:59:31.2903362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:59:31.2903523Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:59:31.2903592Z 2025-03-04T20:59:31.2903956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:59:31.2904097Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:59:31.2904172Z 2025-03-04T20:59:31.2904521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:59:31.2904669Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:59:31.2904802Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:59:31.2904957Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:59:31.2905107Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:59:31.2905193Z 2025-03-04T20:59:31.2905552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:59:31.2905691Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:59:31.2905822Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:59:31.2905973Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:59:31.2906121Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:59:31.2906186Z 2025-03-04T20:59:31.2906527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:59:31.2906651Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:59:31.2906856Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:59:31.2906991Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:59:31.2907064Z 2025-03-04T20:59:31.2907396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:59:31.2907520Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:59:31.2907684Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:59:31.2907841Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:59:31.2907906Z 2025-03-04T20:59:31.2908230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:59:31.2908333Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:59:31.2908459Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:59:31.2908523Z 2025-03-04T20:59:31.2908835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:59:31.2908924Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:59:31.2909044Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:59:31.2909110Z 2025-03-04T20:59:31.2909419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:59:31.2909535Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:59:31.2909667Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:59:31.2909730Z 2025-03-04T20:59:31.2910039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:59:31.2910149Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:59:31.2910281Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:59:31.2910346Z 2025-03-04T20:59:31.2910699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:59:31.2910881Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:59:31.2910956Z 2025-03-04T20:59:31.2911316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:59:31.2911484Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:59:31.2911549Z 2025-03-04T20:59:31.2911939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:59:31.2912124Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:59:31.2912200Z 2025-03-04T20:59:31.2912717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:59:31.2912870Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:59:31.2912946Z 2025-03-04T20:59:31.2913305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:31.2913462Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:59:31.2913530Z 2025-03-04T20:59:31.2913994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:59:31.2914117Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:59:31.2914256Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:59:31.2914380Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:59:31.2914468Z 2025-03-04T20:59:31.2914933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:59:31.2915107Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:59:31.2915342Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:59:31.2915417Z 2025-03-04T20:59:31.2915876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:59:31.2916077Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:59:31.2916152Z 2025-03-04T20:59:31.2916455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:31.2916614Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:59:31.2916679Z 2025-03-04T20:59:31.2917071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:59:31.2917222Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:59:31.2917294Z 2025-03-04T20:59:31.2917596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:59:31.2917767Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:59:31.2917832Z 2025-03-04T20:59:31.2918218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:59:31.2918356Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:59:31.2918430Z 2025-03-04T20:59:31.2918910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:59:31.2919061Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:59:31.2919182Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:59:31.2919348Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:59:31.2919515Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:59:31.2919590Z 2025-03-04T20:59:31.2919982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:59:31.2921249Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:59:31.2921327Z 2025-03-04T20:59:48.8682754Z 2025-03-04T20:59:48.8687756Z class GraphModule(torch.nn.Module): 2025-03-04T20:59:48.8689692Z def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:59:48.8691543Z l_features_res4_ = L_features_res4_ 2025-03-04T20:59:48.8692147Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:59:48.8693654Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:59:48.8695307Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:59:48.8695912Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:59:48.8698694Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:59:48.8699299Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:59:48.8699859Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:59:48.8700237Z 2025-03-04T20:59:48.8700832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:59:48.8701521Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:59:48.8701934Z 2025-03-04T20:59:48.8702350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:48.8702866Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:59:48.8703130Z 2025-03-04T20:59:48.8703678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:59:48.8704343Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:59:48.8704622Z 2025-03-04T20:59:48.8705021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:48.8705524Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:59:48.8705784Z 2025-03-04T20:59:48.8706340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:59:48.8706968Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:59:48.8707310Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:59:48.8707589Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:59:48.8707836Z 2025-03-04T20:59:48.8708267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:59:48.8708828Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:59:48.8709085Z 2025-03-04T20:59:48.8709510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:59:48.8710028Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:59:48.8710275Z 2025-03-04T20:59:48.8710757Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:59:48.8711451Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:59:48.8711773Z 2025-03-04T20:59:48.8712276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:59:48.8712866Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:59:48.8713366Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:59:48.8713854Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:59:48.8714147Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:59:48.8714378Z 2025-03-04T20:59:48.8714769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:59:48.8715247Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T20:59:48.8715484Z 2025-03-04T20:59:48.8715833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:48.8716829Z x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:59:48.8717539Z 2025-03-04T20:59:48.8717908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:59:48.8718427Z x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:59:48.8718730Z 2025-03-04T20:59:48.8719205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:59:48.8720322Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:59:48.8721121Z 2025-03-04T20:59:48.8721577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:59:48.8722618Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:59:48.8723404Z 2025-03-04T20:59:48.8723849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:59:48.8724410Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:59:48.8724766Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:59:48.8725037Z 2025-03-04T20:59:48.8725559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:59:48.8726204Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T20:59:48.8726599Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:59:48.8727012Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:59:48.8727314Z 2025-03-04T20:59:48.8727825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:59:48.8728514Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:59:48.8728845Z 2025-03-04T20:59:48.8729385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:59:48.8730056Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:59:48.8730423Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:59:48.8730776Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:59:48.8731043Z 2025-03-04T20:59:48.8731527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:59:48.8732305Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:59:48.8732638Z 2025-03-04T20:59:48.8733104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:59:48.8733656Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:59:48.8733938Z 2025-03-04T20:59:48.8734409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:59:48.8734948Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:59:48.8735282Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:59:48.8735643Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:59:48.8735918Z 2025-03-04T20:59:48.8736347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:59:48.8736892Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:59:48.8737211Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:59:48.8737551Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:59:48.8737836Z 2025-03-04T20:59:48.8738274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:59:48.8738819Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:59:48.8739121Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:59:48.8739421Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:59:48.8739677Z 2025-03-04T20:59:48.8740110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:59:48.8740645Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:59:48.8740945Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:59:48.8741228Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:59:48.8741481Z 2025-03-04T20:59:48.8741939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:59:48.8742843Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:59:48.8743187Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:59:48.8743427Z 2025-03-04T20:59:48.8743836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:59:48.8744361Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:59:48.8744724Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:59:48.8744980Z 2025-03-04T20:59:48.8745389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:59:48.8745920Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:59:48.8746262Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:59:48.8746514Z 2025-03-04T20:59:48.8746931Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:59:48.8747496Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:59:48.8748012Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:59:48.8748267Z 2025-03-04T20:59:48.8748760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:59:48.8749312Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:59:48.8749583Z 2025-03-04T20:59:48.8750020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:59:48.8750563Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:59:48.8750870Z 2025-03-04T20:59:48.8751372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:59:48.8751976Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:59:48.8752308Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:59:48.8752650Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:59:48.8753009Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:59:48.8753279Z 2025-03-04T20:59:48.8753740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:59:48.8754303Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:59:48.8754631Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:59:48.8754972Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:59:48.8755328Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:59:48.8755594Z 2025-03-04T20:59:48.8756089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:59:48.8756663Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:59:48.8756998Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:59:48.8757361Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:59:48.8757626Z 2025-03-04T20:59:48.8758095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:59:48.8759664Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:59:48.8760024Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:59:48.8760386Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:59:48.8760647Z 2025-03-04T20:59:48.8761072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:59:48.8761560Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:59:48.8761834Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:59:48.8762082Z 2025-03-04T20:59:48.8762500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:59:48.8762976Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:59:48.8763282Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:59:48.8763525Z 2025-03-04T20:59:48.8763946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:59:48.8764446Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:59:48.8764751Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:59:48.8765012Z 2025-03-04T20:59:48.8765421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:59:48.8765906Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:59:48.8766199Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:59:48.8766449Z 2025-03-04T20:59:48.8766888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:59:48.8767470Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:59:48.8767762Z 2025-03-04T20:59:48.8768184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:59:48.8768729Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:59:48.8769013Z 2025-03-04T20:59:48.8769482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:59:48.8770089Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:59:48.8770379Z 2025-03-04T20:59:48.8770946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:59:48.8771648Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:59:48.8772052Z 2025-03-04T20:59:48.8772499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:48.8773072Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:59:48.8773346Z 2025-03-04T20:59:48.8773926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:59:48.8774560Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:59:48.8774834Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:59:48.8775109Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:59:48.8775342Z 2025-03-04T20:59:48.8775897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:59:48.8776587Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:59:48.8777041Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:59:48.8777396Z 2025-03-04T20:59:48.8777976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:59:48.8778649Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:59:48.8778934Z 2025-03-04T20:59:48.8779312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:48.8779833Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:59:48.8780109Z 2025-03-04T20:59:48.8780576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:59:48.8781168Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:59:48.8781438Z 2025-03-04T20:59:48.8781821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:59:48.8782319Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:59:48.8782583Z 2025-03-04T20:59:48.8783044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:59:48.8783614Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:59:48.8783874Z 2025-03-04T20:59:48.8784446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:59:48.8785118Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:59:48.8785429Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:59:48.8785782Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:59:48.8786122Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:59:48.8786377Z 2025-03-04T20:59:48.8786832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:59:48.8787373Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:59:48.8787630Z 2025-03-04T20:59:48.8787779Z 2025-03-04T20:59:48.8787877Z class GraphModule(torch.nn.Module): 2025-03-04T20:59:48.8789867Z def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:59:48.8791443Z l_features_res4_ = L_features_res4_ 2025-03-04T20:59:48.8792354Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:59:48.8794626Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:59:48.8795119Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:59:48.8795659Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:59:48.8796245Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:59:48.8796839Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:59:48.8797384Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:59:48.8797748Z 2025-03-04T20:59:48.8798283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:59:48.8798930Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:59:48.8799201Z 2025-03-04T20:59:48.8799585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:48.8800075Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:59:48.8800330Z 2025-03-04T20:59:48.8800851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:59:48.8801487Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:59:48.8801757Z 2025-03-04T20:59:48.8802137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:48.8802620Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:59:48.8802875Z 2025-03-04T20:59:48.8803335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:59:48.8803944Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:59:48.8804324Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:59:48.8804597Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:59:48.8804840Z 2025-03-04T20:59:48.8805274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:59:48.8805792Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:59:48.8806040Z 2025-03-04T20:59:48.8806464Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:59:48.8806974Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:59:48.8807224Z 2025-03-04T20:59:48.8807701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:59:48.8808393Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:59:48.8808726Z 2025-03-04T20:59:48.8809244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:59:48.8809849Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:59:48.8810357Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:59:48.8810863Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:59:48.8811157Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:59:48.8811392Z 2025-03-04T20:59:48.8811788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:59:48.8812345Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T20:59:48.8812590Z 2025-03-04T20:59:48.8812952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:59:48.8813970Z x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:59:48.8814706Z 2025-03-04T20:59:48.8815076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:59:48.8815598Z x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:59:48.8815919Z 2025-03-04T20:59:48.8816442Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:59:48.8817645Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:59:48.8818531Z 2025-03-04T20:59:48.8819037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:59:48.8820167Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:59:48.8820952Z 2025-03-04T20:59:48.8821430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:59:48.8822039Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:59:48.8822426Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:59:48.8822719Z 2025-03-04T20:59:48.8823322Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:59:48.8824016Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T20:59:48.8824391Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:59:48.8824784Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:59:48.8825098Z 2025-03-04T20:59:48.8825594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:59:48.8826262Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:59:48.8826586Z 2025-03-04T20:59:48.8827107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:59:48.8827749Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:59:48.8828109Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:59:48.8828456Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:59:48.8828716Z 2025-03-04T20:59:48.8829183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:59:48.8829791Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:59:48.8830084Z 2025-03-04T20:59:48.8830484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:59:48.8830997Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:59:48.8831260Z 2025-03-04T20:59:48.8831666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:59:48.8832169Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:59:48.8832488Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:59:48.8832838Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:59:48.8833108Z 2025-03-04T20:59:48.8833517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:59:48.8834015Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:59:48.8834316Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:59:48.8834640Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:59:48.8834909Z 2025-03-04T20:59:48.8835312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:59:48.8835808Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:59:48.8836077Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:59:48.8836363Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:59:48.8836609Z 2025-03-04T20:59:48.8837012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:59:48.8837535Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:59:48.8837828Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:59:48.8838100Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:59:48.8838367Z 2025-03-04T20:59:48.8838791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:59:48.8839321Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:59:48.8839667Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:59:48.8839905Z 2025-03-04T20:59:48.8840299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:59:48.8840829Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:59:48.8841166Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:59:48.8841401Z 2025-03-04T20:59:48.8841802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:59:48.8842840Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:59:48.8843191Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:59:48.8843434Z 2025-03-04T20:59:48.8843852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:59:48.8844411Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:59:48.8844776Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:59:48.8845024Z 2025-03-04T20:59:48.8845469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:59:48.8846025Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:59:48.8846296Z 2025-03-04T20:59:48.8846768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:59:48.8847311Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:59:48.8847575Z 2025-03-04T20:59:48.8848023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:59:48.8848581Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:59:48.8848913Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:59:48.8849261Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:59:48.8849628Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:59:48.8849902Z 2025-03-04T20:59:48.8850429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:59:48.8850990Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:59:48.8851319Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:59:48.8851660Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:59:48.8852103Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:59:48.8852380Z 2025-03-04T20:59:48.8852873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:59:48.8853409Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:59:48.8853755Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:59:48.8854110Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:59:48.8854373Z 2025-03-04T20:59:48.8854810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:59:48.8855329Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:59:48.8855663Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:59:48.8856018Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:59:48.8856273Z 2025-03-04T20:59:48.8856675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:59:48.8857152Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:59:48.8857420Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:59:48.8857662Z 2025-03-04T20:59:48.8858063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:59:48.8858533Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:59:48.8858800Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:59:48.8859036Z 2025-03-04T20:59:48.8859434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:59:48.8859916Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:59:48.8860237Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:59:48.8860495Z 2025-03-04T20:59:48.8860892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:59:48.8861376Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:59:48.8861671Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:59:48.8861922Z 2025-03-04T20:59:48.8862362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:59:48.8863014Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:59:48.8863316Z 2025-03-04T20:59:48.8863788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:59:48.8864344Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:59:48.8864628Z 2025-03-04T20:59:48.8865099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:59:48.8865709Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:59:48.8866004Z 2025-03-04T20:59:48.8866596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:59:48.8867278Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:59:48.8867533Z 2025-03-04T20:59:48.8867934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:48.8868429Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:59:48.8868694Z 2025-03-04T20:59:48.8869217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:59:48.8869825Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:59:48.8870109Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:59:48.8870378Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:59:48.8870608Z 2025-03-04T20:59:48.8871149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:59:48.8871811Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:59:48.8872253Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:59:48.8872591Z 2025-03-04T20:59:48.8873123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:59:48.8873784Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:59:48.8874084Z 2025-03-04T20:59:48.8874466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:48.8874963Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:59:48.8875231Z 2025-03-04T20:59:48.8875694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:59:48.8876272Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:59:48.8876545Z 2025-03-04T20:59:48.8876938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:59:48.8877443Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:59:48.8877703Z 2025-03-04T20:59:48.8878211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:59:48.8878787Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:59:48.8879049Z 2025-03-04T20:59:48.8879641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:59:48.8880347Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:59:48.8880657Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:59:48.8880992Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:59:48.8881340Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:59:48.8881601Z 2025-03-04T20:59:48.8882067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:59:48.8882616Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:59:48.8882861Z 2025-03-04T20:59:51.4662550Z 2025-03-04T20:59:51.4670092Z class GraphModule(torch.nn.Module): 2025-03-04T20:59:51.4671963Z def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"): 2025-03-04T20:59:51.4672760Z l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_ 2025-03-04T20:59:51.4673104Z l_anchors_0_tensor = L_anchors_0_tensor 2025-03-04T20:59:51.4673389Z l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_ 2025-03-04T20:59:51.4673663Z 2025-03-04T20:59:51.4674236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:59:51.4674958Z pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4); l_pred_anchor_deltas_0_ = None 2025-03-04T20:59:51.4675310Z 2025-03-04T20:59:51.4675867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:59:51.4676584Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0); l_anchors_0_tensor = None 2025-03-04T20:59:51.4677280Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:59:51.4677647Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:59:51.4677926Z 2025-03-04T20:59:51.4678412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:59:51.4679019Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float(); pred_anchor_deltas_i = None 2025-03-04T20:59:51.4679315Z 2025-03-04T20:59:51.4679724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:59:51.4680245Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:59:51.4680521Z 2025-03-04T20:59:51.4681037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:59:51.4681552Z getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:59:51.4681866Z getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:59:51.4682195Z widths: "f32[328500][1]cpu" = getitem - getitem_1; getitem = getitem_1 = None 2025-03-04T20:59:51.4682465Z 2025-03-04T20:59:51.4682874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:59:51.4683389Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:59:51.4683740Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:59:51.4684068Z heights: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:59:51.4684345Z 2025-03-04T20:59:51.4684753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:59:51.4685253Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:59:51.4685523Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:59:51.4685790Z ctr_x: "f32[328500][1]cpu" = getitem_4 + mul; getitem_4 = mul = None 2025-03-04T20:59:51.4686040Z 2025-03-04T20:59:51.4686446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:59:51.4686965Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:59:51.4687259Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:59:51.4687534Z ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1; getitem_5 = mul_1 = None 2025-03-04T20:59:51.4687783Z 2025-03-04T20:59:51.4688460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:59:51.4688995Z getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:59:51.4689328Z dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0; getitem_6 = None 2025-03-04T20:59:51.4689569Z 2025-03-04T20:59:51.4689970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:59:51.4690494Z getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:59:51.4698553Z dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0; getitem_7 = None 2025-03-04T20:59:51.4699044Z 2025-03-04T20:59:51.4699555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:59:51.4700108Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:59:51.4700454Z dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:59:51.4700705Z 2025-03-04T20:59:51.4701133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:59:51.4701711Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:59:51.4702093Z dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:59:51.4702330Z 2025-03-04T20:59:51.4702765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:59:51.4703383Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:59:51.4703657Z 2025-03-04T20:59:51.4704084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:59:51.4704613Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:59:51.4704873Z 2025-03-04T20:59:51.4705314Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:59:51.4705905Z getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:59:51.4706232Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10; dx = getitem_10 = None 2025-03-04T20:59:51.4706574Z getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:59:51.4706929Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11; mul_2 = getitem_11 = None 2025-03-04T20:59:51.4707195Z 2025-03-04T20:59:51.4707639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:59:51.4708189Z getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:59:51.4708511Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12; dy = getitem_12 = None 2025-03-04T20:59:51.4708844Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:59:51.4709194Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13; mul_3 = getitem_13 = None 2025-03-04T20:59:51.4709458Z 2025-03-04T20:59:51.4709883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:59:51.4710397Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:59:51.4710732Z getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:59:51.4711083Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14; exp = getitem_14 = None 2025-03-04T20:59:51.4711344Z 2025-03-04T20:59:51.4711772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:59:51.4712278Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:59:51.4712648Z getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:59:51.4713009Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15; exp_1 = getitem_15 = None 2025-03-04T20:59:51.4723857Z 2025-03-04T20:59:51.4724353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:59:51.4724859Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:59:51.4725143Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:59:51.4725394Z 2025-03-04T20:59:51.4725823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:59:51.4726328Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:59:51.4726599Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:59:51.4726848Z 2025-03-04T20:59:51.4727414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:59:51.4727913Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:59:51.4728220Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:59:51.4728479Z 2025-03-04T20:59:51.4728884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:59:51.4729365Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:59:51.4729700Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:59:51.4729951Z 2025-03-04T20:59:51.4730395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:59:51.4730991Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:59:51.4731285Z 2025-03-04T20:59:51.4731826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:59:51.4732432Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:59:51.4732728Z 2025-03-04T20:59:51.4733218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:59:51.4733851Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:59:51.4734158Z 2025-03-04T20:59:51.4734747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:59:51.4735505Z arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:59:51.4735767Z 2025-03-04T20:59:51.4736177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:51.4736691Z batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:59:51.4736962Z 2025-03-04T20:59:51.4737517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:59:51.4738261Z topk = l_pred_objectness_logits_0_.topk(6000, dim = 1); l_pred_objectness_logits_0_ = None 2025-03-04T20:59:51.4738696Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:59:51.4739002Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:59:51.4739249Z 2025-03-04T20:59:51.4739842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:59:51.4740576Z getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:59:51.4741062Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)]; proposals_i_1 = getitem_18 = topk_idx = None 2025-03-04T20:59:51.4741440Z 2025-03-04T20:59:51.4742107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:59:51.4742822Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:59:51.4743124Z 2025-03-04T20:59:51.4743529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:59:51.4744088Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:59:51.4744414Z 2025-03-04T20:59:51.4744888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:59:51.4745480Z getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:59:51.4745750Z 2025-03-04T20:59:51.4746141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:59:51.4746645Z tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32); getitem_20 = None 2025-03-04T20:59:51.4746910Z 2025-03-04T20:59:51.4747378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:59:51.4747952Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:59:51.4748217Z 2025-03-04T20:59:51.4748791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:59:51.4749471Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor); tensor = None 2025-03-04T20:59:51.4749786Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:59:51.4750119Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:59:51.4750470Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:59:51.4750732Z 2025-03-04T20:59:51.4751195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:59:51.4751748Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:59:51.4751990Z 2025-03-04T21:00:15.4906847Z 2025-03-04T21:00:15.4907506Z class GraphModule(torch.nn.Module): 2025-03-04T21:00:15.4909226Z def forward(self, L_stack0_: "f32[3261, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"): 2025-03-04T21:00:15.4916197Z l_stack0_ = L_stack0_ 2025-03-04T21:00:15.4916908Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:00:15.4917987Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:00:15.4924069Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:00:15.4924709Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:00:15.4925209Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:00:15.4925622Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:00:15.4926195Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:00:15.4926586Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:00:15.4926888Z 2025-03-04T21:00:15.4927463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T21:00:15.4928126Z mean: "f32[3261, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T21:00:15.4928394Z 2025-03-04T21:00:15.4928793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:00:15.4929771Z scores: "f32[3261, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:00:15.4930500Z 2025-03-04T21:00:15.4930928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:00:15.4931973Z proposal_deltas: "f32[3261, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:00:15.4932707Z 2025-03-04T21:00:15.4933092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:00:15.4933569Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:00:15.4933968Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:00:15.4934282Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:00:15.4934579Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:00:15.4934861Z getitem_2: "Sym(1261 - s0)" = size_1[0] 2025-03-04T21:00:15.4935130Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:00:15.4935358Z 2025-03-04T21:00:15.4935743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:00:15.4936706Z proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:00:15.4937432Z 2025-03-04T21:00:15.4937907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:00:15.4938544Z deltas: "f32[3261, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:00:15.4938822Z 2025-03-04T21:00:15.4939224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:00:15.4939758Z boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:00:15.4940046Z 2025-03-04T21:00:15.4940456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:00:15.4941024Z getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:00:15.4941336Z getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:15.4941657Z widths: "f32[3261][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:00:15.4941925Z 2025-03-04T21:00:15.4942340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:00:15.4942849Z getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:00:15.4943155Z getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:00:15.4943478Z heights: "f32[3261][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:00:15.4943750Z 2025-03-04T21:00:15.4944161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:00:15.4944655Z getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:15.4944924Z mul: "f32[3261][1]cpu" = 0.5 * widths 2025-03-04T21:00:15.4945193Z ctr_x: "f32[3261][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:00:15.4945441Z 2025-03-04T21:00:15.4945847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:00:15.4946361Z getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:00:15.4946648Z mul_1: "f32[3261][1]cpu" = 0.5 * heights 2025-03-04T21:00:15.4946916Z ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:00:15.4947161Z 2025-03-04T21:00:15.4947605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:00:15.4948143Z getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:00:15.4948480Z dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:00:15.4948719Z 2025-03-04T21:00:15.4949112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:00:15.4949623Z getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:00:15.4949950Z dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:00:15.4950195Z 2025-03-04T21:00:15.4957003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:00:15.4957593Z getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:00:15.4957975Z dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:00:15.4958231Z 2025-03-04T21:00:15.4958756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:00:15.4959299Z getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:00:15.4959647Z dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:00:15.4959882Z 2025-03-04T21:00:15.4960307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:00:15.4960865Z dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:00:15.4961132Z 2025-03-04T21:00:15.4961569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:00:15.4962091Z dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:00:15.4962348Z 2025-03-04T21:00:15.4962788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:00:15.4963329Z getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:00:15.4963658Z mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:00:15.4963987Z getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:00:15.4964328Z pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:00:15.4964581Z 2025-03-04T21:00:15.4965006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:00:15.4965543Z getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:00:15.4965847Z mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:00:15.4966167Z getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:00:15.4966502Z pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:00:15.4966758Z 2025-03-04T21:00:15.4967175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:00:15.4967673Z exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:00:15.4968035Z getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:00:15.4968377Z pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:00:15.4968621Z 2025-03-04T21:00:15.4969031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:00:15.4969527Z exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:00:15.4969864Z getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:00:15.4970219Z pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:00:15.4970473Z 2025-03-04T21:00:15.4970876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:00:15.4971389Z mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:00:15.4971661Z x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:00:15.4971901Z 2025-03-04T21:00:15.4972302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:00:15.4972771Z mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:00:15.4973029Z y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:00:15.4973268Z 2025-03-04T21:00:15.4973663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:00:15.4974366Z mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:00:15.4974682Z x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:00:15.4974951Z 2025-03-04T21:00:15.4975396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:00:15.4975880Z mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:00:15.4976171Z y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:00:15.4976456Z 2025-03-04T21:00:15.4976905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:00:15.4977503Z pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:00:15.4977802Z 2025-03-04T21:00:15.4978235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:00:15.4978801Z predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320)); pred_boxes = None 2025-03-04T21:00:15.4979093Z 2025-03-04T21:00:15.4979544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:00:15.4980166Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:00:15.4980536Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:00:15.4980832Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:00:15.4981141Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:00:15.4981487Z getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:00:15.4981755Z 2025-03-04T21:00:15.4982138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:00:15.4982700Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:00:15.4983055Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:00:15.4983302Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:00:15.4983663Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:00:15.4984019Z getitem_26: "Sym(1261 - s0)" = size_3[0] 2025-03-04T21:00:15.4984266Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:00:15.4984481Z 2025-03-04T21:00:15.4999209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:00:15.4999958Z probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:00:15.5000271Z 2025-03-04T21:00:15.5000755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:00:15.5001381Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:00:15.5001758Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:00:15.5002127Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:00:15.5002444Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:00:15.5002782Z getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:00:15.5003059Z 2025-03-04T21:00:15.5003635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:00:15.5004344Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:00:15.5004703Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:00:15.5005055Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:00:15.5005409Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:00:15.5005718Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:00:15.5005972Z 2025-03-04T21:00:15.5006434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:00:15.5006966Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:00:15.5007217Z 2025-03-04T21:00:15.5007351Z 2025-03-04T21:00:15.5007445Z class GraphModule(torch.nn.Module): 2025-03-04T21:00:15.5008819Z def forward(self, L_stack0_: "f32[3261, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"): 2025-03-04T21:00:15.5017000Z l_stack0_ = L_stack0_ 2025-03-04T21:00:15.5017446Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:00:15.5018041Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:00:15.5018608Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:00:15.5019179Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:00:15.5019666Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:00:15.5020176Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:00:15.5020587Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:00:15.5020990Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:00:15.5021299Z 2025-03-04T21:00:15.5021892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T21:00:15.5022534Z mean: "f32[3261, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T21:00:15.5022823Z 2025-03-04T21:00:15.5023224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:00:15.5024182Z scores: "f32[3261, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:00:15.5024976Z 2025-03-04T21:00:15.5028867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:00:15.5029911Z proposal_deltas: "f32[3261, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:00:15.5031114Z 2025-03-04T21:00:15.5031529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:00:15.5032001Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:00:15.5032261Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:00:15.5032498Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:00:15.5032779Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:00:15.5033045Z getitem_2: "Sym(1261 - s0)" = size_1[0] 2025-03-04T21:00:15.5033293Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:00:15.5033519Z 2025-03-04T21:00:15.5033893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:00:15.5034882Z proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:00:15.5035592Z 2025-03-04T21:00:15.5036051Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:00:15.5036616Z deltas: "f32[3261, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:00:15.5036887Z 2025-03-04T21:00:15.5037279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:00:15.5037791Z boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:00:15.5038070Z 2025-03-04T21:00:15.5038515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:00:15.5039010Z getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:00:15.5039312Z getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:15.5039627Z widths: "f32[3261][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:00:15.5039887Z 2025-03-04T21:00:15.5040290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:00:15.5040802Z getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:00:15.5041660Z getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:00:15.5042006Z heights: "f32[3261][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:00:15.5042286Z 2025-03-04T21:00:15.5042700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:00:15.5043193Z getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:15.5043453Z mul: "f32[3261][1]cpu" = 0.5 * widths 2025-03-04T21:00:15.5043720Z ctr_x: "f32[3261][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:00:15.5043953Z 2025-03-04T21:00:15.5044359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:00:15.5044866Z getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:00:15.5045159Z mul_1: "f32[3261][1]cpu" = 0.5 * heights 2025-03-04T21:00:15.5045429Z ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:00:15.5045671Z 2025-03-04T21:00:15.5046133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:00:15.5046639Z getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:00:15.5046966Z dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:00:15.5047202Z 2025-03-04T21:00:15.5047587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:00:15.5048089Z getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:00:15.5048462Z dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:00:15.5048692Z 2025-03-04T21:00:15.5049076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:00:15.5049569Z getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:00:15.5049884Z dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:00:15.5050120Z 2025-03-04T21:00:15.5050506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:00:15.5051037Z getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:00:15.5051377Z dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:00:15.5051610Z 2025-03-04T21:00:15.5052278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:00:15.5052842Z dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:00:15.5053107Z 2025-03-04T21:00:15.5053528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:00:15.5054164Z dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:00:15.5054435Z 2025-03-04T21:00:15.5054921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:00:15.5055489Z getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:00:15.5055826Z mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:00:15.5056173Z getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:00:15.5056543Z pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:00:15.5056808Z 2025-03-04T21:00:15.5057262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:00:15.5057828Z getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:00:15.5058154Z mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:00:15.5058496Z getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:00:15.5058852Z pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:00:15.5059124Z 2025-03-04T21:00:15.5059570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:00:15.5060100Z exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:00:15.5060437Z getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:00:15.5060788Z pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:00:15.5061052Z 2025-03-04T21:00:15.5061497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:00:15.5062024Z exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:00:15.5062390Z getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:00:15.5063895Z pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:00:15.5064437Z 2025-03-04T21:00:15.5064938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:00:15.5065434Z mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:00:15.5065715Z x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:00:15.5065963Z 2025-03-04T21:00:15.5066381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:00:15.5066867Z mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:00:15.5067139Z y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:00:15.5067375Z 2025-03-04T21:00:15.5067970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:00:15.5068472Z mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:00:15.5068777Z x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:00:15.5069042Z 2025-03-04T21:00:15.5069463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:00:15.5069962Z mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:00:15.5070255Z y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:00:15.5070508Z 2025-03-04T21:00:15.5070948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:00:15.5071530Z pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:00:15.5071822Z 2025-03-04T21:00:15.5072248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:00:15.5072806Z predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320)); pred_boxes = None 2025-03-04T21:00:15.5073095Z 2025-03-04T21:00:15.5073546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:00:15.5074157Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:00:15.5074528Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:00:15.5074823Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:00:15.5075130Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:00:15.5075453Z getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:00:15.5075718Z 2025-03-04T21:00:15.5076105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:00:15.5076664Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:00:15.5077016Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:00:15.5077292Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:00:15.5077681Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:00:15.5078035Z getitem_26: "Sym(1261 - s0)" = size_3[0] 2025-03-04T21:00:15.5078288Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:00:15.5078513Z 2025-03-04T21:00:15.5078943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:00:15.5079510Z probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:00:15.5079802Z 2025-03-04T21:00:15.5080256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:00:15.5080866Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:00:15.5081264Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:00:15.5081567Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:00:15.5081874Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:00:15.5082195Z getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:00:15.5082454Z 2025-03-04T21:00:15.5083018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:00:15.5083816Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:00:15.5084173Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:00:15.5084514Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:00:15.5084863Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:00:15.5085165Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:00:15.5085410Z 2025-03-04T21:00:15.5085858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:00:15.5086385Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:00:15.5086629Z 2025-03-04T21:00:15.5086808Z 2025-03-04T21:00:15.5086902Z class GraphModule(torch.nn.Module): 2025-03-04T21:00:15.5088641Z def forward(self, L_stack0_: "f32[3261, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"): 2025-03-04T21:00:15.5090046Z l_stack0_ = L_stack0_ 2025-03-04T21:00:15.5090444Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:00:15.5091026Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:00:15.5091680Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:00:15.5092237Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:00:15.5092712Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:00:15.5093114Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:00:15.5093517Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:00:15.5093983Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:00:15.5094300Z 2025-03-04T21:00:15.5094870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T21:00:15.5095572Z mean: "f32[3261, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T21:00:15.5095848Z 2025-03-04T21:00:15.5096250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:00:15.5097220Z scores: "f32[3261, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:00:15.5097963Z 2025-03-04T21:00:15.5098381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:00:15.5099404Z proposal_deltas: "f32[3261, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:00:15.5100155Z 2025-03-04T21:00:15.5100535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:00:15.5101008Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:00:15.5101268Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:00:15.5101506Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:00:15.5101786Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:00:15.5102053Z getitem_2: "Sym(1261 - s0)" = size_1[0] 2025-03-04T21:00:15.5102307Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:00:15.5102535Z 2025-03-04T21:00:15.5102900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:00:15.5103850Z proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:00:15.5104562Z 2025-03-04T21:00:15.5105015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:00:15.5105593Z deltas: "f32[3261, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:00:15.5105861Z 2025-03-04T21:00:15.5106252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:00:15.5106770Z boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:00:15.5107046Z 2025-03-04T21:00:15.5107435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:00:15.5107930Z getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:00:15.5108236Z getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:15.5108546Z widths: "f32[3261][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:00:15.5108808Z 2025-03-04T21:00:15.5109256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:00:15.5109746Z getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:00:15.5110036Z getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:00:15.5110345Z heights: "f32[3261][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:00:15.5110601Z 2025-03-04T21:00:15.5110994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:00:15.5111496Z getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:15.5111753Z mul: "f32[3261][1]cpu" = 0.5 * widths 2025-03-04T21:00:15.5112009Z ctr_x: "f32[3261][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:00:15.5112248Z 2025-03-04T21:00:15.5112649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:00:15.5113153Z getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:00:15.5113440Z mul_1: "f32[3261][1]cpu" = 0.5 * heights 2025-03-04T21:00:15.5113706Z ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:00:15.5113948Z 2025-03-04T21:00:15.5114364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:00:15.5114872Z getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:00:15.5115194Z dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:00:15.5115433Z 2025-03-04T21:00:15.5115817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:00:15.5116318Z getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:00:15.5116635Z dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:00:15.5116865Z 2025-03-04T21:00:15.5117248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:00:15.5117743Z getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:00:15.5118064Z dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:00:15.5118295Z 2025-03-04T21:00:15.5118710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:00:15.5119245Z getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:00:15.5119587Z dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:00:15.5119818Z 2025-03-04T21:00:15.5120235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:00:15.5120758Z dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:00:15.5121015Z 2025-03-04T21:00:15.5121424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:00:15.5121940Z dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:00:15.5123215Z 2025-03-04T21:00:15.5123660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:00:15.5124198Z getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:00:15.5124511Z mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:00:15.5124838Z getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:00:15.5125179Z pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:00:15.5125456Z 2025-03-04T21:00:15.5125887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:00:15.5126424Z getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:00:15.5126736Z mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:00:15.5127057Z getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:00:15.5127393Z pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:00:15.5127648Z 2025-03-04T21:00:15.5128066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:00:15.5128567Z exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:00:15.5128894Z getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:00:15.5129234Z pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:00:15.5129487Z 2025-03-04T21:00:15.5129902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:00:15.5130398Z exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:00:15.5130725Z getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:00:15.5131068Z pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:00:15.5131323Z 2025-03-04T21:00:15.5131726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:00:15.5132180Z mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:00:15.5132463Z x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:00:15.5132701Z 2025-03-04T21:00:15.5133091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:00:15.5133574Z mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:00:15.5133933Z y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:00:15.5134185Z 2025-03-04T21:00:15.5134602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:00:15.5135103Z mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:00:15.5135413Z x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:00:15.5135666Z 2025-03-04T21:00:15.5136118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:00:15.5136614Z mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:00:15.5136913Z y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:00:15.5137171Z 2025-03-04T21:00:15.5137625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:00:15.5138226Z pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:00:15.5138547Z 2025-03-04T21:00:15.5138988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:00:15.5139570Z predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320)); pred_boxes = None 2025-03-04T21:00:15.5139871Z 2025-03-04T21:00:15.5140338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:00:15.5140968Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:00:15.5141343Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:00:15.5141646Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:00:15.5141954Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:00:15.5142277Z getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:00:15.5142550Z 2025-03-04T21:00:15.5142949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:00:15.5143521Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:00:15.5143886Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:00:15.5144139Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:00:15.5144522Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:00:15.5144889Z getitem_26: "Sym(1261 - s0)" = size_3[0] 2025-03-04T21:00:15.5145154Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:00:15.5145374Z 2025-03-04T21:00:15.5145789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:00:15.5146359Z probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:00:15.5146654Z 2025-03-04T21:00:15.5147087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:00:15.5147682Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:00:15.5148034Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:00:15.5148323Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:00:15.5148632Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:00:15.5148943Z getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:00:15.5149200Z 2025-03-04T21:00:15.5149772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:00:15.5150439Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:00:15.5150771Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:00:15.5151103Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:00:15.5151439Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:00:15.5151732Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:00:15.5152007Z 2025-03-04T21:00:15.5152441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:00:15.5152952Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:00:15.5153183Z 2025-03-04T21:00:15.5153271Z 2025-03-04T21:00:15.5153367Z class GraphModule(torch.nn.Module): 2025-03-04T21:00:15.5154701Z def forward(self, L_stack0_: "f32[3261, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"): 2025-03-04T21:00:15.5156000Z l_stack0_ = L_stack0_ 2025-03-04T21:00:15.5156379Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:00:15.5156926Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:00:15.5157469Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:00:15.5158013Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:00:15.5158478Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:00:15.5158866Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:00:15.5159275Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:00:15.5159660Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:00:15.5159944Z 2025-03-04T21:00:15.5160460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T21:00:15.5161076Z mean: "f32[3261, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T21:00:15.5161337Z 2025-03-04T21:00:15.5161721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:00:15.5162713Z scores: "f32[3261, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:00:15.5163418Z 2025-03-04T21:00:15.5163821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:00:15.5166757Z proposal_deltas: "f32[3261, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:00:15.5167864Z 2025-03-04T21:00:15.5168275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:00:15.5168750Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:00:15.5169008Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:00:15.5169242Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:00:15.5169513Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:00:15.5169777Z getitem_2: "Sym(1261 - s0)" = size_1[0] 2025-03-04T21:00:15.5170024Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:00:15.5170247Z 2025-03-04T21:00:15.5170619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:00:15.5171549Z proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:00:15.5172255Z 2025-03-04T21:00:15.5172712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:00:15.5173282Z deltas: "f32[3261, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:00:15.5173550Z 2025-03-04T21:00:15.5174035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:00:15.5174614Z boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:00:15.5174913Z 2025-03-04T21:00:15.5175356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:00:15.5175930Z getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:00:15.5176259Z getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:15.5176601Z widths: "f32[3261][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:00:15.5176879Z 2025-03-04T21:00:15.5177312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:00:15.5177851Z getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:00:15.5178170Z getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:00:15.5178508Z heights: "f32[3261][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:00:15.5178796Z 2025-03-04T21:00:15.5179266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:00:15.5179787Z getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:15.5180065Z mul: "f32[3261][1]cpu" = 0.5 * widths 2025-03-04T21:00:15.5180339Z ctr_x: "f32[3261][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:00:15.5180584Z 2025-03-04T21:00:15.5181007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:00:15.5181544Z getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:00:15.5181872Z mul_1: "f32[3261][1]cpu" = 0.5 * heights 2025-03-04T21:00:15.5182153Z ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:00:15.5182412Z 2025-03-04T21:00:15.5182860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:00:15.5183408Z getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:00:15.5183734Z dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:00:15.5183972Z 2025-03-04T21:00:15.5184360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:00:15.5184865Z getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:00:15.5185189Z dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:00:15.5185425Z 2025-03-04T21:00:15.5185815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:00:15.5186319Z getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:00:15.5186640Z dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:00:15.5186873Z 2025-03-04T21:00:15.5187265Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:00:15.5187797Z getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:00:15.5188481Z dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:00:15.5188748Z 2025-03-04T21:00:15.5189198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:00:15.5189819Z dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:00:15.5190075Z 2025-03-04T21:00:15.5190532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:00:15.5191053Z dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:00:15.5191306Z 2025-03-04T21:00:15.5191732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:00:15.5192268Z getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:00:15.5192580Z mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:00:15.5192908Z getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:00:15.5193311Z pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:00:15.5193572Z 2025-03-04T21:00:15.5193997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:00:15.5194522Z getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:00:15.5194832Z mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:00:15.5195152Z getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:00:15.5195519Z pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:00:15.5195771Z 2025-03-04T21:00:15.5196186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:00:15.5196681Z exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:00:15.5196995Z getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:00:15.5197326Z pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:00:15.5197571Z 2025-03-04T21:00:15.5197981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:00:15.5198486Z exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:00:15.5198812Z getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:00:15.5199155Z pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:00:15.5199410Z 2025-03-04T21:00:15.5199794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:00:15.5200246Z mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:00:15.5200507Z x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:00:15.5200739Z 2025-03-04T21:00:15.5201125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:00:15.5201581Z mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:00:15.5201837Z y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:00:15.5202098Z 2025-03-04T21:00:15.5202485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:00:15.5202953Z mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:00:15.5203240Z x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:00:15.5203483Z 2025-03-04T21:00:15.5203866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:00:15.5204325Z mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:00:15.5204604Z y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:00:15.5204847Z 2025-03-04T21:00:15.5205270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:00:15.5205881Z pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:00:15.5206171Z 2025-03-04T21:00:15.5206580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:00:15.5207121Z predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320)); pred_boxes = None 2025-03-04T21:00:15.5207402Z 2025-03-04T21:00:15.5207843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:00:15.5208467Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:00:15.5208827Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:00:15.5209114Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:00:15.5209414Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:00:15.5209728Z getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:00:15.5209986Z 2025-03-04T21:00:15.5210354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:00:15.5210902Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:00:15.5211251Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:00:15.5211489Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:00:15.5211843Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:00:15.5212194Z getitem_26: "Sym(1261 - s0)" = size_3[0] 2025-03-04T21:00:15.5212440Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:00:15.5212655Z 2025-03-04T21:00:15.5213066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:00:15.5214446Z probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:00:15.5214759Z 2025-03-04T21:00:15.5215210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:00:15.5215814Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:00:15.5216216Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:00:15.5216519Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:00:15.5216871Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:00:15.5217196Z getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:00:15.5217461Z 2025-03-04T21:00:15.5218030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:00:15.5218732Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:00:15.5219083Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:00:15.5219426Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:00:15.5219771Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:00:15.5220114Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:00:15.5220360Z 2025-03-04T21:00:15.5220807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:00:15.5221333Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:00:15.5221580Z 2025-03-04T21:00:17.6118668Z 2025-03-04T21:00:17.6122981Z class GraphModule(torch.nn.Module): 2025-03-04T21:00:17.6127819Z def forward(self, L_predictions_0_: "f32[3261, 81][81, 1]cpu", L_predictions_1_: "f32[3261, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"): 2025-03-04T21:00:17.6129033Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:00:17.6129269Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:00:17.6129588Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:00:17.6129991Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:00:17.6130386Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:00:17.6130783Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:00:17.6131087Z 2025-03-04T21:00:17.6131522Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:00:17.6131998Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:00:17.6132271Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:00:17.6138235Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:00:17.6142723Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:00:17.6143218Z getitem_2: "Sym(1261 - s0)" = size_1[0] 2025-03-04T21:00:17.6143553Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:00:17.6146873Z 2025-03-04T21:00:17.6147480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:00:17.6148539Z proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:00:17.6150694Z 2025-03-04T21:00:17.6151280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:00:17.6151950Z deltas: "f32[3261, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:00:17.6156205Z 2025-03-04T21:00:17.6156785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:00:17.6157897Z boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:00:17.6158468Z 2025-03-04T21:00:17.6159418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:00:17.6160057Z getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:00:17.6160620Z getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:17.6160955Z widths: "f32[3261][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:00:17.6161230Z 2025-03-04T21:00:17.6161674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:00:17.6162199Z getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:00:17.6162498Z getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:00:17.6162855Z heights: "f32[3261][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:00:17.6163128Z 2025-03-04T21:00:17.6163537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:00:17.6164040Z getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:17.6164305Z mul: "f32[3261][1]cpu" = 0.5 * widths 2025-03-04T21:00:17.6164570Z ctr_x: "f32[3261][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:00:17.6164821Z 2025-03-04T21:00:17.6165246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:00:17.6165775Z getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:00:17.6166076Z mul_1: "f32[3261][1]cpu" = 0.5 * heights 2025-03-04T21:00:17.6166360Z ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:00:17.6166608Z 2025-03-04T21:00:17.6167068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:00:17.6167609Z getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:00:17.6167951Z dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:00:17.6168196Z 2025-03-04T21:00:17.6168604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:00:17.6169138Z getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:00:17.6169469Z dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:00:17.6169715Z 2025-03-04T21:00:17.6170119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:00:17.6170687Z getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:00:17.6171020Z dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:00:17.6171261Z 2025-03-04T21:00:17.6171665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:00:17.6172218Z getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:00:17.6172578Z dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:00:17.6172827Z 2025-03-04T21:00:17.6173284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:00:17.6174012Z dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:00:17.6174316Z 2025-03-04T21:00:17.6174865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:00:17.6175425Z dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:00:17.6175687Z 2025-03-04T21:00:17.6176132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:00:17.6176688Z getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:00:17.6177043Z mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:00:17.6177382Z getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:00:17.6177754Z pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:00:17.6178028Z 2025-03-04T21:00:17.6178483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:00:17.6179044Z getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:00:17.6179371Z mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:00:17.6179715Z getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:00:17.6180074Z pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:00:17.6180343Z 2025-03-04T21:00:17.6180784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:00:17.6181321Z exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:00:17.6181664Z getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:00:17.6182022Z pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:00:17.6182282Z 2025-03-04T21:00:17.6182720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:00:17.6183251Z exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:00:17.6183598Z getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:00:17.6183964Z pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:00:17.6184254Z 2025-03-04T21:00:17.6184674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:00:17.6185158Z mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:00:17.6185435Z x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:00:17.6185681Z 2025-03-04T21:00:17.6186089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:00:17.6186573Z mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:00:17.6186846Z y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:00:17.6187091Z 2025-03-04T21:00:17.6187503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:00:17.6188032Z mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:00:17.6188504Z x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:00:17.6188758Z 2025-03-04T21:00:17.6189160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:00:17.6189639Z mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:00:17.6189934Z y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:00:17.6190233Z 2025-03-04T21:00:17.6190675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:00:17.6191265Z pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:00:17.6191570Z 2025-03-04T21:00:17.6191997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:00:17.6192566Z predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320)); pred_boxes = None 2025-03-04T21:00:17.6192859Z 2025-03-04T21:00:17.6193317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:00:17.6193936Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:00:17.6194302Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:00:17.6194598Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:00:17.6194911Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:00:17.6195233Z getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:00:17.6195499Z 2025-03-04T21:00:17.6195886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:00:17.6196450Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:00:17.6196806Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:00:17.6197061Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:00:17.6197438Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:00:17.6197877Z getitem_26: "Sym(1261 - s0)" = size_3[0] 2025-03-04T21:00:17.6198164Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:00:17.6198398Z 2025-03-04T21:00:17.6198840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:00:17.6199466Z probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:00:17.6199808Z 2025-03-04T21:00:17.6200274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:00:17.6200869Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:00:17.6201226Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:00:17.6201522Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:00:17.6201890Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:00:17.6202207Z getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:00:17.6202471Z 2025-03-04T21:00:17.6203021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:00:17.6203786Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:00:17.6204150Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:00:17.6204488Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:00:17.6204864Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:00:17.6205163Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:00:17.6205408Z 2025-03-04T21:00:17.6205847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:00:17.6206364Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:00:17.6206598Z 2025-03-04T21:00:17.6206729Z 2025-03-04T21:00:17.6206828Z class GraphModule(torch.nn.Module): 2025-03-04T21:00:17.6207638Z def forward(self, L_predictions_0_: "f32[3261, 81][81, 1]cpu", L_predictions_1_: "f32[3261, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"): 2025-03-04T21:00:17.6208427Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:00:17.6208655Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:00:17.6208971Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:00:17.6209379Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:00:17.6209781Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:00:17.6210185Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:00:17.6210482Z 2025-03-04T21:00:17.6210866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:00:17.6211358Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:00:17.6211621Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:00:17.6211866Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:00:17.6212147Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:00:17.6212411Z getitem_2: "Sym(1261 - s0)" = size_1[0] 2025-03-04T21:00:17.6212665Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:00:17.6212895Z 2025-03-04T21:00:17.6213275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:00:17.6214400Z proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:00:17.6215163Z 2025-03-04T21:00:17.6215667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:00:17.6216257Z deltas: "f32[3261, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:00:17.6216542Z 2025-03-04T21:00:17.6216950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:00:17.6217486Z boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:00:17.6217794Z 2025-03-04T21:00:17.6218201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:00:17.6218709Z getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:00:17.6219021Z getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:17.6219346Z widths: "f32[3261][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:00:17.6219615Z 2025-03-04T21:00:17.6220027Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:00:17.6220528Z getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:00:17.6220829Z getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:00:17.6221150Z heights: "f32[3261][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:00:17.6221427Z 2025-03-04T21:00:17.6221833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:00:17.6222335Z getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:17.6222599Z mul: "f32[3261][1]cpu" = 0.5 * widths 2025-03-04T21:00:17.6222858Z ctr_x: "f32[3261][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:00:17.6223101Z 2025-03-04T21:00:17.6223504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:00:17.6224021Z getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:00:17.6224311Z mul_1: "f32[3261][1]cpu" = 0.5 * heights 2025-03-04T21:00:17.6224581Z ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:00:17.6224827Z 2025-03-04T21:00:17.6225243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:00:17.6225802Z getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:00:17.6226138Z dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:00:17.6226381Z 2025-03-04T21:00:17.6226770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:00:17.6227284Z getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:00:17.6227614Z dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:00:17.6227846Z 2025-03-04T21:00:17.6228239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:00:17.6228753Z getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:00:17.6229111Z dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:00:17.6229349Z 2025-03-04T21:00:17.6229743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:00:17.6230287Z getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:00:17.6230637Z dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:00:17.6230870Z 2025-03-04T21:00:17.6231330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:00:17.6231871Z dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:00:17.6232132Z 2025-03-04T21:00:17.6232559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:00:17.6233084Z dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:00:17.6233345Z 2025-03-04T21:00:17.6233785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:00:17.6234316Z getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:00:17.6234633Z mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:00:17.6234965Z getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:00:17.6235305Z pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:00:17.6235567Z 2025-03-04T21:00:17.6236000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:00:17.6236528Z getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:00:17.6236845Z mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:00:17.6237178Z getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:00:17.6237530Z pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:00:17.6237796Z 2025-03-04T21:00:17.6238217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:00:17.6238758Z exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:00:17.6239090Z getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:00:17.6239445Z pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:00:17.6239705Z 2025-03-04T21:00:17.6240142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:00:17.6240672Z exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:00:17.6241017Z getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:00:17.6241371Z pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:00:17.6241643Z 2025-03-04T21:00:17.6242080Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:00:17.6242544Z mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:00:17.6242812Z x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:00:17.6243050Z 2025-03-04T21:00:17.6243448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:00:17.6243907Z mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:00:17.6244170Z y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:00:17.6244425Z 2025-03-04T21:00:17.6244812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:00:17.6245307Z mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:00:17.6245597Z x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:00:17.6245847Z 2025-03-04T21:00:17.6246238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:00:17.6246719Z mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:00:17.6247007Z y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:00:17.6247259Z 2025-03-04T21:00:17.6247701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:00:17.6248294Z pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:00:17.6248587Z 2025-03-04T21:00:17.6249019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:00:17.6249580Z predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320)); pred_boxes = None 2025-03-04T21:00:17.6249873Z 2025-03-04T21:00:17.6250332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:00:17.6250951Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:00:17.6251321Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:00:17.6251616Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:00:17.6251964Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:00:17.6252291Z getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:00:17.6252561Z 2025-03-04T21:00:17.6252948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:00:17.6253531Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:00:17.6253980Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:00:17.6254245Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:00:17.6254645Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:00:17.6255019Z getitem_26: "Sym(1261 - s0)" = size_3[0] 2025-03-04T21:00:17.6255289Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:00:17.6255530Z 2025-03-04T21:00:17.6256016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:00:17.6256641Z probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:00:17.6256987Z 2025-03-04T21:00:17.6257433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:00:17.6258040Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:00:17.6258431Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:00:17.6258732Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:00:17.6259045Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:00:17.6259368Z getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:00:17.6259633Z 2025-03-04T21:00:17.6260206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:00:17.6260900Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:00:17.6261248Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:00:17.6261594Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:00:17.6261941Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:00:17.6262239Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:00:17.6262485Z 2025-03-04T21:00:17.6262933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:00:17.6263465Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:00:17.6263702Z 2025-03-04T21:00:17.6263833Z 2025-03-04T21:00:17.6263932Z class GraphModule(torch.nn.Module): 2025-03-04T21:00:17.6264749Z def forward(self, L_predictions_0_: "f32[3261, 81][81, 1]cpu", L_predictions_1_: "f32[3261, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"): 2025-03-04T21:00:17.6265573Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:00:17.6265809Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:00:17.6266134Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:00:17.6266543Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:00:17.6266944Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:00:17.6267344Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:00:17.6267644Z 2025-03-04T21:00:17.6268033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:00:17.6268508Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:00:17.6268773Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:00:17.6269011Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:00:17.6270504Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:00:17.6270800Z getitem_2: "Sym(1261 - s0)" = size_1[0] 2025-03-04T21:00:17.6271053Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:00:17.6271302Z 2025-03-04T21:00:17.6271679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:00:17.6272638Z proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:00:17.6273380Z 2025-03-04T21:00:17.6273850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:00:17.6274430Z deltas: "f32[3261, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:00:17.6274710Z 2025-03-04T21:00:17.6275111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:00:17.6275635Z boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:00:17.6275916Z 2025-03-04T21:00:17.6276327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:00:17.6276832Z getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:00:17.6277142Z getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:17.6277457Z widths: "f32[3261][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:00:17.6277721Z 2025-03-04T21:00:17.6278135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:00:17.6278635Z getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:00:17.6278935Z getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:00:17.6279255Z heights: "f32[3261][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:00:17.6279522Z 2025-03-04T21:00:17.6279924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:00:17.6280444Z getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:17.6280714Z mul: "f32[3261][1]cpu" = 0.5 * widths 2025-03-04T21:00:17.6280977Z ctr_x: "f32[3261][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:00:17.6281224Z 2025-03-04T21:00:17.6281636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:00:17.6282146Z getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:00:17.6282434Z mul_1: "f32[3261][1]cpu" = 0.5 * heights 2025-03-04T21:00:17.6282699Z ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:00:17.6282948Z 2025-03-04T21:00:17.6283353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:00:17.6283912Z getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:00:17.6284243Z dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:00:17.6284483Z 2025-03-04T21:00:17.6284883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:00:17.6285383Z getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:00:17.6285707Z dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:00:17.6285961Z 2025-03-04T21:00:17.6286345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:00:17.6286855Z getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:00:17.6287185Z dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:00:17.6287422Z 2025-03-04T21:00:17.6287816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:00:17.6288581Z getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:00:17.6288942Z dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:00:17.6289180Z 2025-03-04T21:00:17.6289610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:00:17.6290152Z dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:00:17.6290422Z 2025-03-04T21:00:17.6290847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:00:17.6291373Z dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:00:17.6291633Z 2025-03-04T21:00:17.6292072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:00:17.6292619Z getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:00:17.6292941Z mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:00:17.6293277Z getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:00:17.6293724Z pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:00:17.6294005Z 2025-03-04T21:00:17.6294459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:00:17.6295042Z getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:00:17.6295380Z mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:00:17.6295716Z getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:00:17.6296068Z pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:00:17.6296336Z 2025-03-04T21:00:17.6296781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:00:17.6297295Z exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:00:17.6297686Z getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:00:17.6298034Z pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:00:17.6298293Z 2025-03-04T21:00:17.6298716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:00:17.6299223Z exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:00:17.6299558Z getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:00:17.6299949Z pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:00:17.6300209Z 2025-03-04T21:00:17.6300620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:00:17.6301087Z mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:00:17.6301354Z x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:00:17.6301593Z 2025-03-04T21:00:17.6301994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:00:17.6302459Z mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:00:17.6302727Z y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:00:17.6302966Z 2025-03-04T21:00:17.6303362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:00:17.6303839Z mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:00:17.6304149Z x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:00:17.6304403Z 2025-03-04T21:00:17.6304799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:00:17.6305275Z mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:00:17.6305567Z y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:00:17.6305816Z 2025-03-04T21:00:17.6306253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:00:17.6306848Z pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:00:17.6307176Z 2025-03-04T21:00:17.6307612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:00:17.6308177Z predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320)); pred_boxes = None 2025-03-04T21:00:17.6308467Z 2025-03-04T21:00:17.6308923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:00:17.6309542Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:00:17.6309913Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:00:17.6310205Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:00:17.6310516Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:00:17.6310874Z getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:00:17.6311133Z 2025-03-04T21:00:17.6311517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:00:17.6312079Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:00:17.6312436Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:00:17.6312686Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:00:17.6313055Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:00:17.6313435Z getitem_26: "Sym(1261 - s0)" = size_3[0] 2025-03-04T21:00:17.6313688Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:00:17.6313910Z 2025-03-04T21:00:17.6314337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:00:17.6314949Z probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:00:17.6315268Z 2025-03-04T21:00:17.6315713Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:00:17.6316319Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:00:17.6316692Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:00:17.6316980Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:00:17.6317278Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:00:17.6317591Z getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:00:17.6317851Z 2025-03-04T21:00:17.6318402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:00:17.6319106Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:00:17.6319445Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:00:17.6319779Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:00:17.6320121Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:00:17.6320441Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:00:17.6320680Z 2025-03-04T21:00:17.6321114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:00:17.6321640Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:00:17.6321880Z 2025-03-04T21:00:19.8425607Z 2025-03-04T21:00:19.8430486Z class GraphModule(torch.nn.Module): 2025-03-04T21:00:19.8434790Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T21:00:19.8438534Z l_scores_0_ = L_scores_0_ 2025-03-04T21:00:19.8442750Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:00:19.8446442Z 2025-03-04T21:00:19.8447120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:00:19.8448240Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:00:19.8448581Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:00:19.8448906Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:00:19.8449228Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:00:19.8449522Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:00:19.8449764Z 2025-03-04T21:00:19.8450222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:00:19.8450799Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:00:19.8451040Z 2025-03-04T21:00:19.8451129Z 2025-03-04T21:00:19.8451221Z class GraphModule(torch.nn.Module): 2025-03-04T21:00:19.8451531Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T21:00:19.8451829Z l_scores_0_ = L_scores_0_ 2025-03-04T21:00:19.8452037Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:00:19.8452227Z 2025-03-04T21:00:19.8452773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:00:19.8453427Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:00:19.8453885Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:00:19.8454217Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:00:19.8454547Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:00:19.8454890Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:00:19.8455135Z 2025-03-04T21:00:19.8455591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:00:19.8456123Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:00:19.8456363Z 2025-03-04T21:00:52.0777215Z Compilation time (from dynamo_timed): 80.97644552 2025-03-04T21:00:52.0777731Z pass 2025-03-04T21:00:52.0782084Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:00:52.0783122Z TIMING: entire_frame_compile:80.97645 gc:0.05162 _recursive_pre_grad_passes:0.03591 _recursive_joint_graph_passes:0.26833 _recursive_post_grad_passes:0.2725 async_compile.wait:38.20362 code_gen:50.48876 inductor_compile:54.539 backend_compile:65.0166 total_wall_time:80.97645 2025-03-04T21:00:52.0784437Z STATS: call_* op count: 777 | FakeTensorMode.__torch_dispatch__:33337 | FakeTensor.__torch_dispatch__:3860 | ProxyTorchDispatchMode.__torch_dispatch__:12067 | attempt fast:91 | slow no contiguity match:44 | fast is_contiguous:47 2025-03-04T21:00:52.0785072Z Dynamo produced 53 graphs covering 777 ops with 42 graph breaks (6 unique) 2025-03-04T21:00:58.5457791Z 2025-03-04T21:01:09.6624466Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:01:09.6627378Z loading model: 0it [00:11, ?it/s] 2025-03-04T21:01:09.6636817Z cpu eval detectron2_fasterrcnn_r_101_dc5 2025-03-04T21:01:28.1782098Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_101_dc5. Setting accuracy check to cosine 2025-03-04T21:01:28.2302422Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:01:43.1924450Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:01:59.7142750Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:02:11.4446787Z 2025-03-04T21:02:11.4452905Z class GraphModule(torch.nn.Module): 2025-03-04T21:02:11.4566078Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", 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1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:02:11.4667380Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:02:11.4667952Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4668814Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4669707Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4670533Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4671356Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4672077Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4672931Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4673826Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4674671Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4675563Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4676298Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4677100Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4677960Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4678804Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4679659Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4680398Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4681215Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4682010Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4682803Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4683546Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4684252Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:02:11.4684979Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4685754Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4686507Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4687243Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4687920Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4688726Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4689470Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4690194Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4690943Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4691605Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4692309Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4693060Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4693841Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4694639Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4695362Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4696074Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4696851Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4697577Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4698276Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4698941Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4699637Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4700385Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4701113Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4701810Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4702472Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4703166Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4703913Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4704658Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4705361Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4706027Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4706728Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4707478Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4708238Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4708937Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4709595Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4710290Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4711048Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4711770Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4712471Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4713130Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4713826Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4714571Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4715302Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4715996Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4716663Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4717373Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4718179Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4718900Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4719600Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4720283Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:02:11.4721007Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4721820Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4722584Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4723314Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4723996Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4724708Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4725488Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4726204Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4726896Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4727558Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4728249Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4728992Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4729713Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4730407Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4731073Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4731770Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4732540Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4733261Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4733970Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4734777Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4735593Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4736418Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4737279Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4738109Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4738873Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4739673Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4740549Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4741385Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4742183Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4742925Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4743713Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4744522Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4745247Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4745928Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4746575Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4747289Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4748018Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4748726Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4749426Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4750092Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4750820Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4751572Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4752298Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4752975Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4753651Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4754350Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4755098Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4755819Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4756518Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4757183Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4757881Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4758629Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4759349Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4760044Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4760693Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4761413Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4762148Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4762857Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4763538Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4764197Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4764919Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4765666Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4766389Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4767103Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4767784Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:02:11.4768504Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4769279Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4770031Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4770761Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4771467Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4772196Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4772979Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4773739Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4774528Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4775315Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4776050Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4776836Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4777604Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4778343Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4779072Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4779796Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4780600Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4781322Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4782038Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4782700Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4783391Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4784137Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4784857Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4785557Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4786218Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4786913Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4787659Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4788532Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4789296Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4789969Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4790670Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4791429Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4792168Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4792921Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4793592Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4794286Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4795039Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4795796Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4796501Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4797166Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4797859Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4798600Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4799329Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4800015Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4800662Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4801337Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4802068Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4802769Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4803462Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4804097Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4804772Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4805503Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4806218Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4806962Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4807636Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4808337Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4809090Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4809831Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4810535Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4811203Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4811900Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4812644Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4813371Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4814071Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4814789Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4815551Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4816311Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4817100Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4817844Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4818553Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4819284Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4820074Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4820879Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4821617Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4822316Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4823044Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4823853Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4824622Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4825367Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4826047Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4826738Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4827493Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4828224Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4828921Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4829587Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4830279Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4831024Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4831770Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4832472Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4833143Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4833838Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4834592Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4835345Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4836040Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4836701Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4837416Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4838185Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4838908Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4839608Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4840268Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4840971Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4841721Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4842454Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4843160Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4843820Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4844517Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4845284Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4846008Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4846703Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4847391Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4848084Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4848869Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4849600Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4850305Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4850971Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4851688Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4852442Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4853164Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4853857Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4854620Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4855406Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4856218Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4857041Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4857832Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4858590Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4859370Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4860244Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4861064Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4861851Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4862604Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4863353Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4864130Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4864857Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4865559Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4866243Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4866947Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4867694Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4868414Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4869114Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4869791Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4870504Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4871265Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4872003Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4872710Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4873383Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4874123Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4874900Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4875638Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4876342Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4877023Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4877789Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4878555Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4879283Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4879983Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4880669Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4881374Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4882130Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4882858Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4883559Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4884228Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4884926Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4885671Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4886392Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4887121Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4887822Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4888735Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4889545Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4890333Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4891098Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4891910Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4892673Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4893485Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4894311Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4895113Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4895846Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4896588Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4897400Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4898174Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4898927Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4899650Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4900387Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4901193Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4901966Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4902707Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4903476Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4904221Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4905027Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4905786Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4906531Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4907205Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4907911Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4908669Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4909415Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4910125Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4910795Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4911494Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4912246Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4912980Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4913688Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4914358Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4915056Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4915809Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4916537Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4917267Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4917937Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4918636Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4919382Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4920105Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4920817Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4921477Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4922157Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4922921Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4923658Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4924355Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4925024Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4925721Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4926469Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4927196Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4927896Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4928568Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4929268Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4930024Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4930775Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4931480Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4932148Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4932844Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4933595Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4934508Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4935339Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4936026Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4936722Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4937502Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4938242Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4938957Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4939631Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4940348Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4941108Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4941843Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4942549Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4943215Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4943915Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4944687Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4945419Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4946126Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4946793Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4947498Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4948290Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4949011Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4949707Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4950368Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4951103Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4951855Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4952616Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4953300Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4953954Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4954635Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4955365Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4956070Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4956748Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4957398Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4958115Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4958921Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4959659Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4960386Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4961067Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4961819Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4962581Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4963314Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4964025Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4964708Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4965420Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4966168Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4966892Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4967595Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4968251Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4968936Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4969692Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4970415Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4971114Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4971785Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4972501Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4973249Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4973970Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4974844Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4975611Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4976375Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4977129Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4977862Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4978589Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4979264Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4979972Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4980734Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4981471Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4982174Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4982850Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4983554Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4984308Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4985044Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4985747Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4986431Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4987124Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4987868Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4988719Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4989433Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4990185Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.4990888Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4991642Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4992368Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4993095Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4993772Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.4994481Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4995248Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4995971Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.4996689Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.4997375Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.4998089Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.4998815Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.4999526Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5000252Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5000918Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5001612Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5002365Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5003097Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5003850Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5004517Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5005207Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5005953Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5006696Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5007407Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5008099Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5008853Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5009645Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5010419Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5011168Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5011869Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5012602Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5013390Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5014167Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5015009Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5015722Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5016477Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5017283Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5018064Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5018847Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5019576Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5020340Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5021172Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5021949Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5022691Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5023440Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:02:11.5024218Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5025049Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5025803Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5026533Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5027215Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5027914Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5028660Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5029405Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5030106Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5030774Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5031469Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5032223Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5032978Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5033677Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5034338Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5035037Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5035805Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5036530Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5037229Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5037924Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5038663Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5039446Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5040210Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5040939Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5041633Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5042374Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5043163Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5043940Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5044680Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5045377Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5046119Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5046958Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5047792Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5048590Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5049422Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:02:11.5050281Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:02:11.5051078Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:02:11.5051926Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:02:11.5053797Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:02:11.5055259Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:02:11.5056116Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:02:11.5056620Z 2025-03-04T21:02:11.5057045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5057891Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5058533Z 2025-03-04T21:02:11.5058919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5060809Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5062506Z 2025-03-04T21:02:11.5062911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:02:11.5063434Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:02:11.5063716Z 2025-03-04T21:02:11.5064246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:02:11.5064907Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:02:11.5065268Z 2025-03-04T21:02:11.5065621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5066372Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5066963Z 2025-03-04T21:02:11.5067351Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5069318Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5071054Z 2025-03-04T21:02:11.5071463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5071987Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:02:11.5072284Z 2025-03-04T21:02:11.5072652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5073479Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5074075Z 2025-03-04T21:02:11.5074455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5076429Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5078129Z 2025-03-04T21:02:11.5078533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5079087Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:02:11.5079369Z 2025-03-04T21:02:11.5079726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5080483Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5081079Z 2025-03-04T21:02:11.5081471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5083460Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5085207Z 2025-03-04T21:02:11.5085573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5086384Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:02:11.5086982Z 2025-03-04T21:02:11.5087363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5089523Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5091363Z 2025-03-04T21:02:11.5091765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5092283Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:02:11.5092570Z 2025-03-04T21:02:11.5092970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5093505Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:02:11.5093804Z 2025-03-04T21:02:11.5094267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5095115Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5095725Z 2025-03-04T21:02:11.5096114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5098093Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5099796Z 2025-03-04T21:02:11.5100177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5100673Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:02:11.5100948Z 2025-03-04T21:02:11.5101295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5102045Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5102625Z 2025-03-04T21:02:11.5103012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5104991Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5106753Z 2025-03-04T21:02:11.5107148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5107665Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:02:11.5107944Z 2025-03-04T21:02:11.5108299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5109869Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5110450Z 2025-03-04T21:02:11.5110809Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5112680Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5114422Z 2025-03-04T21:02:11.5114810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5115333Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:02:11.5115628Z 2025-03-04T21:02:11.5116031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5116534Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:02:11.5116825Z 2025-03-04T21:02:11.5117185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5117967Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5118542Z 2025-03-04T21:02:11.5118916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5120904Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5122668Z 2025-03-04T21:02:11.5123065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5123583Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:02:11.5123867Z 2025-03-04T21:02:11.5124264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5125043Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5125616Z 2025-03-04T21:02:11.5125975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5127852Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5129482Z 2025-03-04T21:02:11.5129856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5130343Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:02:11.5130611Z 2025-03-04T21:02:11.5130955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5131703Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5132259Z 2025-03-04T21:02:11.5132619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5134650Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5136466Z 2025-03-04T21:02:11.5136864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5137395Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:02:11.5137691Z 2025-03-04T21:02:11.5138124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5138663Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:02:11.5138961Z 2025-03-04T21:02:11.5139325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5140114Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5140712Z 2025-03-04T21:02:11.5141088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5143023Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5144753Z 2025-03-04T21:02:11.5145150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5145674Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:02:11.5145958Z 2025-03-04T21:02:11.5146316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5147097Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5147674Z 2025-03-04T21:02:11.5148045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5149992Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5151760Z 2025-03-04T21:02:11.5152160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5152685Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:02:11.5152999Z 2025-03-04T21:02:11.5153368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5154166Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5154759Z 2025-03-04T21:02:11.5155133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5157126Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5158860Z 2025-03-04T21:02:11.5159221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5160024Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:02:11.5160635Z 2025-03-04T21:02:11.5160994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5162908Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5164648Z 2025-03-04T21:02:11.5165018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5165513Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:02:11.5165786Z 2025-03-04T21:02:11.5166167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5166669Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:02:11.5166959Z 2025-03-04T21:02:11.5167343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5168088Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5168632Z 2025-03-04T21:02:11.5169007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5170970Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5172713Z 2025-03-04T21:02:11.5173108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5173623Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:02:11.5173908Z 2025-03-04T21:02:11.5174364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5175250Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5175861Z 2025-03-04T21:02:11.5176247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5178328Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5180148Z 2025-03-04T21:02:11.5180567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5181112Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:02:11.5181409Z 2025-03-04T21:02:11.5181785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5182657Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5183275Z 2025-03-04T21:02:11.5183669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5185740Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5187384Z 2025-03-04T21:02:11.5187749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5188408Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:02:11.5188697Z 2025-03-04T21:02:11.5189078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5189587Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:02:11.5189874Z 2025-03-04T21:02:11.5190223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5190975Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5191527Z 2025-03-04T21:02:11.5191891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5193792Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5195469Z 2025-03-04T21:02:11.5195856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5196351Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:02:11.5196623Z 2025-03-04T21:02:11.5197021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5197783Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5198331Z 2025-03-04T21:02:11.5198689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5200583Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5202215Z 2025-03-04T21:02:11.5202599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5203093Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:02:11.5203356Z 2025-03-04T21:02:11.5203690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5204418Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5204959Z 2025-03-04T21:02:11.5205305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5207130Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5208750Z 2025-03-04T21:02:11.5209114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5209617Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:02:11.5209899Z 2025-03-04T21:02:11.5210277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5210810Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:02:11.5211081Z 2025-03-04T21:02:11.5211416Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5212150Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5212709Z 2025-03-04T21:02:11.5213065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5215094Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5216741Z 2025-03-04T21:02:11.5217106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5217583Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:02:11.5217843Z 2025-03-04T21:02:11.5218174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5218900Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5219434Z 2025-03-04T21:02:11.5219782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5221579Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5223182Z 2025-03-04T21:02:11.5223549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5224033Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:02:11.5224295Z 2025-03-04T21:02:11.5224674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5225411Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5225950Z 2025-03-04T21:02:11.5226299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5228172Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5229815Z 2025-03-04T21:02:11.5230188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5230687Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:02:11.5230970Z 2025-03-04T21:02:11.5231349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5231831Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:02:11.5232100Z 2025-03-04T21:02:11.5232435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5233157Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5233684Z 2025-03-04T21:02:11.5234030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5235852Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5237433Z 2025-03-04T21:02:11.5237822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5238292Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:02:11.5238547Z 2025-03-04T21:02:11.5238881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5239598Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5240150Z 2025-03-04T21:02:11.5240497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5242324Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5243937Z 2025-03-04T21:02:11.5244306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5244791Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:02:11.5245054Z 2025-03-04T21:02:11.5245392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5246127Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5246667Z 2025-03-04T21:02:11.5247020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5248831Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5250434Z 2025-03-04T21:02:11.5250782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5251571Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:02:11.5252139Z 2025-03-04T21:02:11.5252498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5254560Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5256358Z 2025-03-04T21:02:11.5256736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5257218Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:02:11.5257485Z 2025-03-04T21:02:11.5257861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5258358Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:02:11.5258632Z 2025-03-04T21:02:11.5258979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5259712Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5260253Z 2025-03-04T21:02:11.5260605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5262439Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5264095Z 2025-03-04T21:02:11.5264476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5264968Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:02:11.5265236Z 2025-03-04T21:02:11.5265608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5266346Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5266889Z 2025-03-04T21:02:11.5267246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5269090Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5270699Z 2025-03-04T21:02:11.5271066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5271545Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:02:11.5271805Z 2025-03-04T21:02:11.5272145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5272883Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5273426Z 2025-03-04T21:02:11.5273776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5275622Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5277272Z 2025-03-04T21:02:11.5277641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5278124Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:02:11.5278398Z 2025-03-04T21:02:11.5278773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5279310Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:02:11.5279581Z 2025-03-04T21:02:11.5279925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5280654Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5281188Z 2025-03-04T21:02:11.5281543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5283395Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5285099Z 2025-03-04T21:02:11.5285477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5285982Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:02:11.5286259Z 2025-03-04T21:02:11.5286622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5287405Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5287979Z 2025-03-04T21:02:11.5288477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5290451Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5292232Z 2025-03-04T21:02:11.5292652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5293188Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:02:11.5293479Z 2025-03-04T21:02:11.5293906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5294814Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5295443Z 2025-03-04T21:02:11.5295831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5297783Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5299537Z 2025-03-04T21:02:11.5299931Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5300448Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:02:11.5300712Z 2025-03-04T21:02:11.5301086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5301577Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:02:11.5301843Z 2025-03-04T21:02:11.5302186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5302918Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5303454Z 2025-03-04T21:02:11.5303808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5305677Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5307291Z 2025-03-04T21:02:11.5307664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5308174Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:02:11.5308438Z 2025-03-04T21:02:11.5308782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5309516Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5310054Z 2025-03-04T21:02:11.5310412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5312291Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5313880Z 2025-03-04T21:02:11.5314251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5314730Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:02:11.5314988Z 2025-03-04T21:02:11.5315326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5316049Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5316580Z 2025-03-04T21:02:11.5316930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5318768Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5320418Z 2025-03-04T21:02:11.5320804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5321297Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:02:11.5321568Z 2025-03-04T21:02:11.5321970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5322460Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:02:11.5322723Z 2025-03-04T21:02:11.5323067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5323795Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5324345Z 2025-03-04T21:02:11.5324697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5326518Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5328123Z 2025-03-04T21:02:11.5328500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5328981Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:02:11.5329241Z 2025-03-04T21:02:11.5329577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5330307Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5330845Z 2025-03-04T21:02:11.5331196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5333110Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5334932Z 2025-03-04T21:02:11.5335348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5335885Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:02:11.5336147Z 2025-03-04T21:02:11.5336487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5337228Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5337771Z 2025-03-04T21:02:11.5338130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5339987Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5341593Z 2025-03-04T21:02:11.5341961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5342448Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:02:11.5342718Z 2025-03-04T21:02:11.5343095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5343582Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:02:11.5343846Z 2025-03-04T21:02:11.5344190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5344916Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5345449Z 2025-03-04T21:02:11.5345843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5347659Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5349271Z 2025-03-04T21:02:11.5349663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5350137Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:02:11.5350393Z 2025-03-04T21:02:11.5350727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5351441Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5351983Z 2025-03-04T21:02:11.5352328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5354171Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5355783Z 2025-03-04T21:02:11.5356161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5356644Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:02:11.5356902Z 2025-03-04T21:02:11.5357240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5357979Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5358521Z 2025-03-04T21:02:11.5358873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5360746Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5362368Z 2025-03-04T21:02:11.5362734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5363255Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:02:11.5363526Z 2025-03-04T21:02:11.5363903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5364397Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:02:11.5364663Z 2025-03-04T21:02:11.5365005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5365736Z x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5366288Z 2025-03-04T21:02:11.5366644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5368480Z x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5370118Z 2025-03-04T21:02:11.5370493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5370973Z out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:02:11.5371233Z 2025-03-04T21:02:11.5371573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5372308Z x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5372850Z 2025-03-04T21:02:11.5373237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5375177Z x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5376974Z 2025-03-04T21:02:11.5377401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5377911Z out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:02:11.5378189Z 2025-03-04T21:02:11.5378553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5379335Z x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5379934Z 2025-03-04T21:02:11.5380311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5382279Z x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5384014Z 2025-03-04T21:02:11.5384406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5384926Z x_93 += out_51; out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T21:02:11.5385212Z 2025-03-04T21:02:11.5385602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5386118Z out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:02:11.5386397Z 2025-03-04T21:02:11.5386756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5387533Z x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5388286Z 2025-03-04T21:02:11.5388680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5390672Z x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5392290Z 2025-03-04T21:02:11.5392664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5393138Z out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T21:02:11.5393397Z 2025-03-04T21:02:11.5393739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5394480Z x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5395054Z 2025-03-04T21:02:11.5395412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5397250Z x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5398882Z 2025-03-04T21:02:11.5399249Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5399734Z out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:02:11.5399996Z 2025-03-04T21:02:11.5400338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5401081Z x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5401630Z 2025-03-04T21:02:11.5402010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5403883Z x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5405519Z 2025-03-04T21:02:11.5405923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5406416Z x_99 += out_55; out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T21:02:11.5406688Z 2025-03-04T21:02:11.5407066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5407557Z out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:02:11.5407818Z 2025-03-04T21:02:11.5408155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5408944Z x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5409474Z 2025-03-04T21:02:11.5409833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5411702Z x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5413360Z 2025-03-04T21:02:11.5413734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5414270Z out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T21:02:11.5414579Z 2025-03-04T21:02:11.5414957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5415794Z x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5416362Z 2025-03-04T21:02:11.5416722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5418609Z x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5418687Z 2025-03-04T21:02:11.5418984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5419127Z out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:02:11.5419201Z 2025-03-04T21:02:11.5419455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5419884Z x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5419970Z 2025-03-04T21:02:11.5420249Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5421800Z x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5421869Z 2025-03-04T21:02:11.5422165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5422319Z x_105 += out_59; out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T21:02:11.5422391Z 2025-03-04T21:02:11.5422684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5422836Z out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:02:11.5422902Z 2025-03-04T21:02:11.5423160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5423603Z x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5423676Z 2025-03-04T21:02:11.5423941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5425534Z x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5425612Z 2025-03-04T21:02:11.5425898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5426046Z out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T21:02:11.5426111Z 2025-03-04T21:02:11.5426367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5426818Z x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5426893Z 2025-03-04T21:02:11.5427160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5428702Z x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5428778Z 2025-03-04T21:02:11.5429067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5429214Z out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T21:02:11.5429279Z 2025-03-04T21:02:11.5429541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5429969Z x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5430064Z 2025-03-04T21:02:11.5430331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5431878Z x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5431959Z 2025-03-04T21:02:11.5432241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5432401Z x_111 += out_63; out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T21:02:11.5432467Z 2025-03-04T21:02:11.5432761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5432922Z out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T21:02:11.5432995Z 2025-03-04T21:02:11.5433250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5433682Z x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5433749Z 2025-03-04T21:02:11.5434024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5435565Z x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5435633Z 2025-03-04T21:02:11.5435927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5436065Z out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T21:02:11.5436139Z 2025-03-04T21:02:11.5436391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5436899Z x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5436973Z 2025-03-04T21:02:11.5437238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5438789Z x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5438857Z 2025-03-04T21:02:11.5439148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5439282Z out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T21:02:11.5439354Z 2025-03-04T21:02:11.5439612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5440036Z x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5440111Z 2025-03-04T21:02:11.5440368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5441861Z x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5441928Z 2025-03-04T21:02:11.5442208Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5442354Z x_117 += out_67; out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T21:02:11.5442427Z 2025-03-04T21:02:11.5442700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5442848Z out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T21:02:11.5442938Z 2025-03-04T21:02:11.5443184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5443592Z x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5443656Z 2025-03-04T21:02:11.5443920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5445423Z x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5445499Z 2025-03-04T21:02:11.5445783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5445931Z out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T21:02:11.5446003Z 2025-03-04T21:02:11.5446250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5446676Z x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5446741Z 2025-03-04T21:02:11.5447005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5448480Z x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5448554Z 2025-03-04T21:02:11.5448837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5448970Z out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T21:02:11.5449040Z 2025-03-04T21:02:11.5449285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5449739Z x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5449803Z 2025-03-04T21:02:11.5450070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5451632Z x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5451709Z 2025-03-04T21:02:11.5451995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5452145Z x_123 += out_71; out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T21:02:11.5452232Z 2025-03-04T21:02:11.5452518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5452668Z out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T21:02:11.5452735Z 2025-03-04T21:02:11.5452991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5453412Z x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5453484Z 2025-03-04T21:02:11.5453752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5455442Z x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5455525Z 2025-03-04T21:02:11.5455827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5455976Z out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T21:02:11.5456077Z 2025-03-04T21:02:11.5456352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5456794Z x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5456869Z 2025-03-04T21:02:11.5457132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5479001Z x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5479229Z 2025-03-04T21:02:11.5479627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5479881Z out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T21:02:11.5479966Z 2025-03-04T21:02:11.5480267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5480748Z x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5480829Z 2025-03-04T21:02:11.5481118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5482720Z x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5482804Z 2025-03-04T21:02:11.5483102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5483278Z x_129 += out_75; out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T21:02:11.5483349Z 2025-03-04T21:02:11.5483706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5483864Z out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T21:02:11.5483944Z 2025-03-04T21:02:11.5484210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5484665Z x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5484734Z 2025-03-04T21:02:11.5485018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5486662Z x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5486750Z 2025-03-04T21:02:11.5487057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5487210Z out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T21:02:11.5487290Z 2025-03-04T21:02:11.5487554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5488010Z x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5488245Z 2025-03-04T21:02:11.5488548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5490156Z x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5490232Z 2025-03-04T21:02:11.5490534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5490738Z out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T21:02:11.5490816Z 2025-03-04T21:02:11.5491077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5491532Z x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5491598Z 2025-03-04T21:02:11.5491887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5493584Z x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5493658Z 2025-03-04T21:02:11.5493997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5494158Z x_135 += out_79; out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T21:02:11.5494293Z 2025-03-04T21:02:11.5494603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5494768Z out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T21:02:11.5494834Z 2025-03-04T21:02:11.5495097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5495519Z x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5495595Z 2025-03-04T21:02:11.5495862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5497404Z x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5497502Z 2025-03-04T21:02:11.5497788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5497937Z out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T21:02:11.5498003Z 2025-03-04T21:02:11.5498262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5498690Z x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5498767Z 2025-03-04T21:02:11.5499031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5500622Z x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5500714Z 2025-03-04T21:02:11.5501000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5501151Z out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T21:02:11.5501216Z 2025-03-04T21:02:11.5501477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5501904Z x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5501980Z 2025-03-04T21:02:11.5502251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5503775Z x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5503852Z 2025-03-04T21:02:11.5504132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5504317Z x_141 += out_83; out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T21:02:11.5504383Z 2025-03-04T21:02:11.5504682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5504827Z out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T21:02:11.5504903Z 2025-03-04T21:02:11.5505157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5505591Z x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5505669Z 2025-03-04T21:02:11.5505968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5507495Z x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5507600Z 2025-03-04T21:02:11.5507887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5508034Z out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T21:02:11.5508103Z 2025-03-04T21:02:11.5508364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5508795Z x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5508873Z 2025-03-04T21:02:11.5509144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5510692Z x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5510786Z 2025-03-04T21:02:11.5511075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5511222Z out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T21:02:11.5511287Z 2025-03-04T21:02:11.5511545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5511974Z x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5512050Z 2025-03-04T21:02:11.5512314Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5513973Z x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5514068Z 2025-03-04T21:02:11.5514354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5514517Z x_147 += out_87; out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T21:02:11.5514582Z 2025-03-04T21:02:11.5514892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5515043Z out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T21:02:11.5515121Z 2025-03-04T21:02:11.5515386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5515841Z x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5515912Z 2025-03-04T21:02:11.5516201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5517831Z x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5517923Z 2025-03-04T21:02:11.5518234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5518378Z out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T21:02:11.5518456Z 2025-03-04T21:02:11.5518723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5519181Z x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5519260Z 2025-03-04T21:02:11.5519573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5521184Z x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5521271Z 2025-03-04T21:02:11.5521580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5521721Z out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T21:02:11.5521806Z 2025-03-04T21:02:11.5522050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5522468Z x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5522544Z 2025-03-04T21:02:11.5522804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5524307Z x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5524395Z 2025-03-04T21:02:11.5524678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5524828Z x_153 += out_91; out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T21:02:11.5524899Z 2025-03-04T21:02:11.5525178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5525323Z out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T21:02:11.5525394Z 2025-03-04T21:02:11.5525642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5526095Z x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5526161Z 2025-03-04T21:02:11.5526429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5531852Z x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5532010Z 2025-03-04T21:02:11.5532744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5532929Z out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T21:02:11.5533010Z 2025-03-04T21:02:11.5533614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5534454Z x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5535192Z 2025-03-04T21:02:11.5536462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5539516Z x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5539649Z 2025-03-04T21:02:11.5539980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5540129Z out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T21:02:11.5540207Z 2025-03-04T21:02:11.5540489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5541010Z x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5541082Z 2025-03-04T21:02:11.5541364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5543132Z x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5543431Z 2025-03-04T21:02:11.5543972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5544142Z x_159 += out_95; out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T21:02:11.5544220Z 2025-03-04T21:02:11.5544516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5544677Z out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T21:02:11.5544745Z 2025-03-04T21:02:11.5545014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5545453Z x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5545531Z 2025-03-04T21:02:11.5545803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5547389Z x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5547490Z 2025-03-04T21:02:11.5547782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5547937Z out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T21:02:11.5548005Z 2025-03-04T21:02:11.5548272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5548749Z x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5548826Z 2025-03-04T21:02:11.5549099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5550679Z x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5550775Z 2025-03-04T21:02:11.5551067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5551218Z out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T21:02:11.5551287Z 2025-03-04T21:02:11.5551553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5552006Z x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5552081Z 2025-03-04T21:02:11.5552353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5553919Z x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5554008Z 2025-03-04T21:02:11.5554295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5554460Z x_165 += out_99; out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T21:02:11.5554528Z 2025-03-04T21:02:11.5554825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5554981Z out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T21:02:11.5555059Z 2025-03-04T21:02:11.5555357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5555794Z x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5555859Z 2025-03-04T21:02:11.5556134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5557678Z x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5557745Z 2025-03-04T21:02:11.5558039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5558179Z out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T21:02:11.5558252Z 2025-03-04T21:02:11.5558504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5558940Z x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5559013Z 2025-03-04T21:02:11.5559280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5560818Z x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5560903Z 2025-03-04T21:02:11.5561198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5561337Z out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T21:02:11.5561409Z 2025-03-04T21:02:11.5561662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5562806Z x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5562884Z 2025-03-04T21:02:11.5563147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5564671Z x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5564759Z 2025-03-04T21:02:11.5565884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5566077Z x_171 += out_103; out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T21:02:11.5566159Z 2025-03-04T21:02:11.5566652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5566828Z out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T21:02:11.5566902Z 2025-03-04T21:02:11.5567335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5567786Z x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5567858Z 2025-03-04T21:02:11.5568330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5570931Z x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5571047Z 2025-03-04T21:02:11.5571534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5571932Z out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T21:02:11.5572296Z 2025-03-04T21:02:11.5573719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5576638Z x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5576726Z 2025-03-04T21:02:11.5577063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5578746Z x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5578819Z 2025-03-04T21:02:11.5579131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5579282Z out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T21:02:11.5579358Z 2025-03-04T21:02:11.5579627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5580098Z x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5580168Z 2025-03-04T21:02:11.5580459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5582106Z x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5582195Z 2025-03-04T21:02:11.5582504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5582678Z x_177 += out_107; out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T21:02:11.5582753Z 2025-03-04T21:02:11.5583085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5583253Z out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T21:02:11.5583322Z 2025-03-04T21:02:11.5583594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5584043Z x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5584140Z 2025-03-04T21:02:11.5584421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5586034Z x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5586110Z 2025-03-04T21:02:11.5586396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5586540Z out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T21:02:11.5586605Z 2025-03-04T21:02:11.5586861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5587292Z x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5587366Z 2025-03-04T21:02:11.5587631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5589295Z x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5589376Z 2025-03-04T21:02:11.5589663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5589895Z out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T21:02:11.5589963Z 2025-03-04T21:02:11.5590224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5590658Z x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5590732Z 2025-03-04T21:02:11.5591022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5592565Z x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5592643Z 2025-03-04T21:02:11.5592921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5593094Z x_183 += out_111; out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T21:02:11.5593161Z 2025-03-04T21:02:11.5593452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5593597Z out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T21:02:11.5593673Z 2025-03-04T21:02:11.5593924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5594352Z x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5594455Z 2025-03-04T21:02:11.5594724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5596254Z x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5596321Z 2025-03-04T21:02:11.5596674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5596816Z out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T21:02:11.5596892Z 2025-03-04T21:02:11.5597147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5597587Z x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5597681Z 2025-03-04T21:02:11.5597947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5599490Z x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5599559Z 2025-03-04T21:02:11.5599851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5599997Z out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T21:02:11.5600065Z 2025-03-04T21:02:11.5600324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5600754Z x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5600828Z 2025-03-04T21:02:11.5601092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5602653Z x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5602727Z 2025-03-04T21:02:11.5603037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5603207Z x_189 += out_115; out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T21:02:11.5603274Z 2025-03-04T21:02:11.5603569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5603715Z out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T21:02:11.5603791Z 2025-03-04T21:02:11.5604045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5604503Z x_190: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5604573Z 2025-03-04T21:02:11.5604845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5606369Z x_191: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_190 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5606444Z 2025-03-04T21:02:11.5606742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5606881Z out_120: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_191); x_191 = None 2025-03-04T21:02:11.5606953Z 2025-03-04T21:02:11.5607204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5607646Z x_192: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_120 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5607731Z 2025-03-04T21:02:11.5608006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5609577Z x_193: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_192 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5609656Z 2025-03-04T21:02:11.5609944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5610084Z out_121: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_193); x_193 = None 2025-03-04T21:02:11.5610155Z 2025-03-04T21:02:11.5610406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5610845Z x_194: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_121 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5610940Z 2025-03-04T21:02:11.5611215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5612741Z x_195: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_194 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5612820Z 2025-03-04T21:02:11.5613095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5613561Z x_196: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_119 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:02:11.5613637Z 2025-03-04T21:02:11.5613916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5615822Z x_197: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_196 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5615933Z 2025-03-04T21:02:11.5616233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5616408Z x_195 += x_197; out_122: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_195; x_195 = x_197 = None 2025-03-04T21:02:11.5616479Z 2025-03-04T21:02:11.5616823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5616985Z out_123: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_122); out_122 = None 2025-03-04T21:02:11.5617064Z 2025-03-04T21:02:11.5617333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5617783Z x_198: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5617870Z 2025-03-04T21:02:11.5618157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5619787Z x_199: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_198 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5619860Z 2025-03-04T21:02:11.5620170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5620316Z out_124: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_199); x_199 = None 2025-03-04T21:02:11.5620393Z 2025-03-04T21:02:11.5620657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5621123Z x_200: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_124 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5621194Z 2025-03-04T21:02:11.5621478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5623101Z x_201: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_200 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5623173Z 2025-03-04T21:02:11.5623484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5623697Z out_125: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_201); x_201 = None 2025-03-04T21:02:11.5623776Z 2025-03-04T21:02:11.5624049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5624519Z x_202: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_125 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5624606Z 2025-03-04T21:02:11.5624895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5626419Z x_203: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_202 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5626487Z 2025-03-04T21:02:11.5626772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5626934Z x_203 += out_123; out_126: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_203; x_203 = out_123 = None 2025-03-04T21:02:11.5627006Z 2025-03-04T21:02:11.5627287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5627443Z out_127: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_126); out_126 = None 2025-03-04T21:02:11.5627519Z 2025-03-04T21:02:11.5627768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5628169Z x_204: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5628262Z 2025-03-04T21:02:11.5628528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5630017Z x_205: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_204 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5630092Z 2025-03-04T21:02:11.5630403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5630548Z out_128: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_205); x_205 = None 2025-03-04T21:02:11.5630612Z 2025-03-04T21:02:11.5630864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5631284Z x_206: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_128 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5631374Z 2025-03-04T21:02:11.5631643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5633129Z x_207: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_206 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5633204Z 2025-03-04T21:02:11.5633483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5633627Z out_129: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_207); x_207 = None 2025-03-04T21:02:11.5633692Z 2025-03-04T21:02:11.5633943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5634366Z x_208: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_129 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5634438Z 2025-03-04T21:02:11.5634705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5636217Z x_209: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_208 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5636293Z 2025-03-04T21:02:11.5636576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5636778Z x_209 += out_127; out_130: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_209; x_209 = out_127 = None 2025-03-04T21:02:11.5636847Z 2025-03-04T21:02:11.5637139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5637286Z out_131: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_130); out_130 = None 2025-03-04T21:02:11.5637358Z 2025-03-04T21:02:11.5637807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:02:11.5637978Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:02:11.5638053Z 2025-03-04T21:02:11.5638351Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:11.5638495Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:02:11.5638562Z 2025-03-04T21:02:11.5639004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:02:11.5639157Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:02:11.5639230Z 2025-03-04T21:02:11.5639521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:11.5639668Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:02:11.5639733Z 2025-03-04T21:02:11.5640112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:02:11.5640293Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:02:11.5640400Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:02:11.5640521Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:02:11.5640593Z 2025-03-04T21:02:11.5640924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:02:11.5641061Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:02:11.5641145Z 2025-03-04T21:02:11.5641484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:02:11.5641605Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:02:11.5641676Z 2025-03-04T21:02:11.5642072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:02:11.5642296Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:02:11.5642361Z 2025-03-04T21:02:11.5642779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:02:11.5642908Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:02:11.5643374Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:02:11.5643502Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:02:11.5643628Z x_210: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:02:11.5643693Z 2025-03-04T21:02:11.5644013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:02:11.5644156Z tensor: "f32[82125, 4][4, 1]cpu" = x_210.to(torch.float32); x_210 = None 2025-03-04T21:02:11.5644229Z 2025-03-04T21:02:11.5644480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5645254Z x_211: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_131, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_131 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:02:11.5645325Z 2025-03-04T21:02:11.5645592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:02:11.5645791Z x_212: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_211, inplace = False); x_211 = None 2025-03-04T21:02:11.5645856Z 2025-03-04T21:02:11.5646236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:02:11.5647068Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:02:11.5647144Z 2025-03-04T21:02:11.5647493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:02:11.5648317Z x_213: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_212 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:02:11.5648389Z 2025-03-04T21:02:11.5648721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:02:11.5648880Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:02:11.5649018Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:02:11.5649091Z 2025-03-04T21:02:11.5649532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:02:11.5649699Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_213.view(4, -1, 4, 73, 75); x_213 = None 2025-03-04T21:02:11.5649869Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:02:11.5650051Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:02:11.5650115Z 2025-03-04T21:02:11.5650534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:02:11.5650745Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:02:11.5650822Z 2025-03-04T21:02:11.5651239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:02:11.5651394Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:02:11.5651545Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:02:11.5651683Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:02:11.5651750Z 2025-03-04T21:02:11.5652136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:02:11.5652314Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:02:11.5652382Z 2025-03-04T21:02:11.5652701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:02:11.5652840Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:02:11.5652912Z 2025-03-04T21:02:11.5653228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:02:11.5653368Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:02:11.5653494Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:11.5653644Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:02:11.5653731Z 2025-03-04T21:02:11.5654067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:02:11.5654192Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:02:11.5654419Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:02:11.5654573Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:02:11.5654654Z 2025-03-04T21:02:11.5655018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:02:11.5655169Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:11.5655284Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:02:11.5655435Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:02:11.5655507Z 2025-03-04T21:02:11.5655905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:02:11.5656069Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:02:11.5656181Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:02:11.5656332Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:02:11.5656409Z 2025-03-04T21:02:11.5656809Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:02:11.5656995Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:02:11.5657116Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:02:11.5657189Z 2025-03-04T21:02:11.5657504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:02:11.5657668Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:02:11.5657783Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:02:11.5657856Z 2025-03-04T21:02:11.5658162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:02:11.5658329Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:02:11.5658443Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:02:11.5658519Z 2025-03-04T21:02:11.5658836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:02:11.5659036Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:02:11.5659162Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:02:11.5659230Z 2025-03-04T21:02:11.5659587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:02:11.5659735Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:02:11.5659813Z 2025-03-04T21:02:11.5660158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:02:11.5660329Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:02:11.5660398Z 2025-03-04T21:02:11.5660762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:02:11.5660905Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:02:11.5661041Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:02:11.5661199Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:02:11.5661353Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:02:11.5661420Z 2025-03-04T21:02:11.5661781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:02:11.5661965Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:02:11.5662105Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:02:11.5662262Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:02:11.5662410Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:02:11.5662477Z 2025-03-04T21:02:11.5662827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:02:11.5662971Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:02:11.5663146Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:02:11.5663285Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:02:11.5663361Z 2025-03-04T21:02:11.5663700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:02:11.5663828Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:02:11.5663996Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:02:11.5664142Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:02:11.5664210Z 2025-03-04T21:02:11.5664541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:02:11.5664640Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:02:11.5664772Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:02:11.5664841Z 2025-03-04T21:02:11.5665169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:02:11.5665266Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:02:11.5665392Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:02:11.5665460Z 2025-03-04T21:02:11.5665779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:02:11.5665900Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:02:11.5666035Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:02:11.5666121Z 2025-03-04T21:02:11.5666444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:02:11.5666564Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:02:11.5666700Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:02:11.5666768Z 2025-03-04T21:02:11.5667136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:02:11.5667321Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:02:11.5667396Z 2025-03-04T21:02:11.5667740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:02:11.5667947Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:02:11.5668017Z 2025-03-04T21:02:11.5668419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:02:11.5668609Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:02:11.5668677Z 2025-03-04T21:02:11.5669180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:02:11.5669337Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:02:11.5669415Z 2025-03-04T21:02:11.5669724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:11.5669877Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:02:11.5669944Z 2025-03-04T21:02:11.5670403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:02:11.5670521Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:02:11.5670636Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:02:11.5670757Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:02:11.5670831Z 2025-03-04T21:02:11.5671308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:02:11.5671489Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:02:11.5671733Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:02:11.5671807Z 2025-03-04T21:02:11.5672279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:02:11.5672461Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:02:11.5672527Z 2025-03-04T21:02:11.5672858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:11.5673019Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:02:11.5673096Z 2025-03-04T21:02:11.5673487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:02:11.5673648Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:02:11.5673714Z 2025-03-04T21:02:11.5674031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:02:11.5674178Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:02:11.5674252Z 2025-03-04T21:02:11.5674664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:02:11.5674812Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:02:11.5674876Z 2025-03-04T21:02:11.5675371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:02:11.5675517Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:02:11.5675654Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:02:11.5675818Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:02:11.5675952Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:02:11.5676025Z 2025-03-04T21:02:11.5676394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:02:11.5676520Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:02:11.5676586Z 2025-03-04T21:02:11.5677142Z 2025-03-04T21:02:11.5677247Z class GraphModule(torch.nn.Module): 2025-03-04T21:02:11.5779470Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", 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"f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:02:11.5780595Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:02:11.5780956Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5781372Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5781771Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5782166Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5782546Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5782921Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5783335Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5783744Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5784192Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5784613Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5784969Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5785386Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5785819Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5786206Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5786586Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5786932Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5787348Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5787755Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5788239Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5788631Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5789021Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:02:11.5789440Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5789854Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5790302Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5790694Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5791024Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5791440Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5791878Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5792278Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5792656Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5792980Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5793418Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5793844Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5794240Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5794580Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5794868Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5795209Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5795551Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5795865Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5796177Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5796456Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5796800Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5797150Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5797470Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5797778Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5798059Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5798457Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5798875Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5799246Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5799601Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5799909Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5800272Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5800615Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5800935Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5801240Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5801531Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5801871Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5802212Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5802527Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5802840Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5803122Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5803464Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5803828Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5804145Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5804454Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5804733Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5805106Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5805443Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5805765Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5806072Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5806389Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:02:11.5806746Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5807088Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5807421Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5807739Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5808024Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5808360Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5808694Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5809006Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5809319Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5809605Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5809961Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5810301Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5810615Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5810926Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5811207Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5811581Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5811918Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5812241Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5812558Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5812861Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5813214Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5813556Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5813884Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5814251Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5814585Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5814972Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5815362Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5815737Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5816067Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5816407Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5816751Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5817096Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5817419Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5817740Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5818061Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5818419Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5818769Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5819092Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5819433Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5819728Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5820082Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5820420Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5820753Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5821070Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5821371Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5821726Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5822067Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5822398Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5822717Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5823035Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5823378Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5823727Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5824042Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5824357Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5824674Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5825016Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5825355Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5825693Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5826006Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5826286Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5826627Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5826952Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5827270Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5827582Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5827872Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:02:11.5828223Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5828566Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5828900Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5829242Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5829530Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5829863Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5830201Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5830521Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5830856Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5831142Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5831476Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5831812Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5832148Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5832465Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5832744Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5833085Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5833424Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5833740Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5834061Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5834341Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5834683Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5835018Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5835960Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5836264Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5836552Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5836903Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5837233Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5837593Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5837902Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5838191Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5838527Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5838890Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5839208Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5839523Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5839811Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5840146Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5840485Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5840802Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5841114Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5841393Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5841736Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5842067Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5842412Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5842724Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5843006Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5843349Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5843679Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5844038Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5844347Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5844635Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5844972Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5845326Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5845652Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5845958Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5846245Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5846579Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5846918Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5847236Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5847552Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5847842Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5848193Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5848564Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5848885Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5849209Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5849506Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5849856Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5850271Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5850611Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5850935Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5851230Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5851608Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5851945Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5852266Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5852571Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5852858Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5853193Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5853535Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5853848Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5854173Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5854598Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5855000Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5855421Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5855765Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5856079Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5856360Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5856707Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5857070Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5857395Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5857711Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5857990Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5858354Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5858691Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5859008Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5859315Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5859603Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5859943Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5860279Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5860601Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5860905Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5861195Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5861552Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5861890Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5862203Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5862516Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5862798Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5863170Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5863514Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5863829Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5864140Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5864434Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5864778Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5865108Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5865427Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5865732Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5866018Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5866361Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5866694Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5867016Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5867317Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5867605Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5867966Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5868308Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5868623Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5868941Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5869235Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5869603Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5869943Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5870257Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5870585Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5870867Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5871214Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5871540Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5871861Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5872175Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5872457Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5872800Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5873131Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5873449Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5873757Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5874073Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5874416Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5874755Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5875083Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5875392Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5875723Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5876070Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5876412Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5876733Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5877069Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5877362Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5877713Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5878053Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5878367Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5878684Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5878970Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5879315Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5879651Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5879975Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5880310Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5880593Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5880937Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5881272Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5881595Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5881932Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5882221Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5882555Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5882891Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5883247Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5883553Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5883837Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5884169Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5884505Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5884818Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5885131Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5885410Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5885749Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5886087Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5886399Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5886734Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5887019Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5887363Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5887698Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5888025Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5888581Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5888883Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5889233Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5889605Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5889941Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5890257Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5890557Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5890903Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5891252Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5891581Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5891908Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5892208Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5892559Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5892922Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5893291Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5893631Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5893918Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5894344Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5894702Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5895085Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5895431Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5895725Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5896090Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5896455Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5896791Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5897107Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5897408Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5897760Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5898123Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5898463Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5898783Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5899083Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5899436Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5899827Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5900159Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5900482Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5900773Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5901132Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5901514Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5901844Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5902163Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5902449Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5902823Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5903167Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5903496Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5903816Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5904111Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5904467Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5904813Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5905146Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5905458Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5905756Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5906101Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5906486Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5906814Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5907138Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5907439Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5907818Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5908168Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5908492Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5908810Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5909114Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5909471Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5909822Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5910143Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5910455Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5910738Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5911086Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5911418Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5911738Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5912045Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5912339Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5912696Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5913035Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5913355Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5913661Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5913949Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5914315Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5914656Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5914968Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5915302Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5915584Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5915930Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5916270Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5916584Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5916898Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5917184Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5917528Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5917859Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5918181Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5918488Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5918795Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5919136Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5919461Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5919780Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5920086Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5920402Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5920745Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5921086Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5921403Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5921744Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5922042Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5922378Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5922717Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5923035Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5923354Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5923636Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5923984Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5924315Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5924640Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5924973Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5925254Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5925597Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5925931Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5926254Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5926592Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5926882Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5927213Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5927550Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5927893Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5928202Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5928489Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5928823Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5929159Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5929474Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5929790Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5930072Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5930413Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5930757Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5931072Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5931408Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5931689Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5932036Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5932369Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5932734Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5933053Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5933349Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5933705Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5934076Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5934479Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5934799Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5935097Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5935507Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5935848Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5936175Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5936497Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5936811Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5937208Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5937606Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5937998Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5938369Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5938677Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5939095Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5939477Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5939892Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5940250Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5940559Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5940960Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5941367Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5941745Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5942088Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5942404Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5942805Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5943202Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5943587Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5943930Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5944259Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:02:11.5944622Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5945014Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5945358Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5945712Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5946020Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5946394Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5946802Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5947137Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5947475Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5947769Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5948158Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5948513Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5948832Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5949144Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5949422Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5949766Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5950100Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5950419Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5950724Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5951010Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.5951347Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5951705Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5952028Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5952334Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5952629Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.5953017Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5953361Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5953674Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5953987Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5954282Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.5954631Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.5954969Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.5955281Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.5955592Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.5955937Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:02:11.5956262Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:02:11.5956567Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:02:11.5956941Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:02:11.5957291Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:02:11.5957647Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:02:11.5958008Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:02:11.5958081Z 2025-03-04T21:02:11.5958380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5958851Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5958928Z 2025-03-04T21:02:11.5959206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5960695Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5960786Z 2025-03-04T21:02:11.5961068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:02:11.5961221Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:02:11.5961287Z 2025-03-04T21:02:11.5961649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:02:11.5961884Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:02:11.5961956Z 2025-03-04T21:02:11.5962207Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5962629Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5962694Z 2025-03-04T21:02:11.5962966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5964475Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5964567Z 2025-03-04T21:02:11.5964867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5965009Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:02:11.5965080Z 2025-03-04T21:02:11.5965338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5965774Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5965842Z 2025-03-04T21:02:11.5966149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5967675Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5967766Z 2025-03-04T21:02:11.5968063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5968206Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:02:11.5968278Z 2025-03-04T21:02:11.5968530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5968976Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5969043Z 2025-03-04T21:02:11.5969328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5970826Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5970918Z 2025-03-04T21:02:11.5971194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5971626Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:02:11.5971697Z 2025-03-04T21:02:11.5971961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5973624Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5973703Z 2025-03-04T21:02:11.5973985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5974163Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:02:11.5974292Z 2025-03-04T21:02:11.5974634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5974804Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:02:11.5974885Z 2025-03-04T21:02:11.5975167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5975643Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5975714Z 2025-03-04T21:02:11.5976004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5977524Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5977592Z 2025-03-04T21:02:11.5977881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5978051Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:02:11.5978124Z 2025-03-04T21:02:11.5978369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5978791Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5978858Z 2025-03-04T21:02:11.5979128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5980650Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5980732Z 2025-03-04T21:02:11.5981020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5981162Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:02:11.5981238Z 2025-03-04T21:02:11.5981483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5981930Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5981994Z 2025-03-04T21:02:11.5982265Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5983813Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5983879Z 2025-03-04T21:02:11.5984162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5984316Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:02:11.5984404Z 2025-03-04T21:02:11.5984684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5984839Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:02:11.5984903Z 2025-03-04T21:02:11.5985156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5985572Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5985645Z 2025-03-04T21:02:11.5985905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5987423Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5987513Z 2025-03-04T21:02:11.5987797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5987943Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:02:11.5988007Z 2025-03-04T21:02:11.5988375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5988801Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5988876Z 2025-03-04T21:02:11.5989143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5990674Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5990750Z 2025-03-04T21:02:11.5991052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5991239Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:02:11.5991302Z 2025-03-04T21:02:11.5991556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5991997Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.5992069Z 2025-03-04T21:02:11.5992352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5993876Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5993980Z 2025-03-04T21:02:11.5994254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.5994423Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:02:11.5994489Z 2025-03-04T21:02:11.5994771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5994925Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:02:11.5994994Z 2025-03-04T21:02:11.5995241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5995659Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.5995732Z 2025-03-04T21:02:11.5995993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.5997473Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.5997564Z 2025-03-04T21:02:11.5997853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.5998005Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:02:11.5998068Z 2025-03-04T21:02:11.5998321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.5998749Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.5998821Z 2025-03-04T21:02:11.5999079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6000609Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6000701Z 2025-03-04T21:02:11.6000982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6001133Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:02:11.6001198Z 2025-03-04T21:02:11.6001450Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6001875Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6001948Z 2025-03-04T21:02:11.6002210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6003724Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6003799Z 2025-03-04T21:02:11.6004066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6004513Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:02:11.6004577Z 2025-03-04T21:02:11.6004845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6006408Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6006487Z 2025-03-04T21:02:11.6006770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6006935Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:02:11.6007008Z 2025-03-04T21:02:11.6007290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6007453Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:02:11.6007516Z 2025-03-04T21:02:11.6007769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6008191Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6008266Z 2025-03-04T21:02:11.6008525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6010041Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6010114Z 2025-03-04T21:02:11.6010395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6010561Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:02:11.6010625Z 2025-03-04T21:02:11.6010882Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6011304Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6011376Z 2025-03-04T21:02:11.6011636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6013172Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6013264Z 2025-03-04T21:02:11.6013552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6013704Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:02:11.6013769Z 2025-03-04T21:02:11.6014034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6014529Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6014610Z 2025-03-04T21:02:11.6014879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6016421Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6016496Z 2025-03-04T21:02:11.6016776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6016942Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:02:11.6017028Z 2025-03-04T21:02:11.6017326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6017479Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:02:11.6017553Z 2025-03-04T21:02:11.6017804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6018240Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6018308Z 2025-03-04T21:02:11.6018583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6020134Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6020223Z 2025-03-04T21:02:11.6020524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6020669Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:02:11.6020740Z 2025-03-04T21:02:11.6020993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6021433Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6021500Z 2025-03-04T21:02:11.6021772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6023313Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6023380Z 2025-03-04T21:02:11.6023675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6023838Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:02:11.6023910Z 2025-03-04T21:02:11.6024162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6024604Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6024670Z 2025-03-04T21:02:11.6024944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6026565Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6026650Z 2025-03-04T21:02:11.6026942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6027102Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:02:11.6027178Z 2025-03-04T21:02:11.6027462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6027623Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:02:11.6027688Z 2025-03-04T21:02:11.6027948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6028378Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6028455Z 2025-03-04T21:02:11.6028724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6030238Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6030330Z 2025-03-04T21:02:11.6030612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6030760Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:02:11.6030825Z 2025-03-04T21:02:11.6031078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6031503Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6031578Z 2025-03-04T21:02:11.6031846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6033369Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6033462Z 2025-03-04T21:02:11.6033743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6033893Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:02:11.6033956Z 2025-03-04T21:02:11.6034209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6034641Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6034707Z 2025-03-04T21:02:11.6034970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6039413Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6039507Z 2025-03-04T21:02:11.6039814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6039980Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:02:11.6040047Z 2025-03-04T21:02:11.6040342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6040493Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:02:11.6040569Z 2025-03-04T21:02:11.6040817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6041286Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6041354Z 2025-03-04T21:02:11.6041651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6043138Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6043225Z 2025-03-04T21:02:11.6043511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6043645Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:02:11.6043716Z 2025-03-04T21:02:11.6043962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6044385Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6044452Z 2025-03-04T21:02:11.6044718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6046278Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6046363Z 2025-03-04T21:02:11.6046664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6046805Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:02:11.6046878Z 2025-03-04T21:02:11.6047134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6047577Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6047644Z 2025-03-04T21:02:11.6047930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6049506Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6049589Z 2025-03-04T21:02:11.6049859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6050302Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:02:11.6050374Z 2025-03-04T21:02:11.6050644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6052248Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6052327Z 2025-03-04T21:02:11.6052613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6052795Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:02:11.6052878Z 2025-03-04T21:02:11.6053174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6053316Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:02:11.6053391Z 2025-03-04T21:02:11.6053646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6054070Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6054143Z 2025-03-04T21:02:11.6054495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6056111Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6056196Z 2025-03-04T21:02:11.6056498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6056639Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:02:11.6056713Z 2025-03-04T21:02:11.6056969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6057403Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6057476Z 2025-03-04T21:02:11.6057746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6059263Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6059331Z 2025-03-04T21:02:11.6059694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6059846Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:02:11.6059921Z 2025-03-04T21:02:11.6060170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6060600Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6060676Z 2025-03-04T21:02:11.6060940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6062472Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6062541Z 2025-03-04T21:02:11.6062846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6063005Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:02:11.6063071Z 2025-03-04T21:02:11.6063366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6063510Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:02:11.6063593Z 2025-03-04T21:02:11.6063838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6064250Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6064317Z 2025-03-04T21:02:11.6064584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6066085Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6066179Z 2025-03-04T21:02:11.6066470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6066601Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:02:11.6066681Z 2025-03-04T21:02:11.6066925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6067350Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6067415Z 2025-03-04T21:02:11.6067681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6069172Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6069258Z 2025-03-04T21:02:11.6069542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6069674Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:02:11.6069745Z 2025-03-04T21:02:11.6069987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6070407Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6070470Z 2025-03-04T21:02:11.6070735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6072208Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6072282Z 2025-03-04T21:02:11.6072577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6072749Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:02:11.6072822Z 2025-03-04T21:02:11.6073102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6073247Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:02:11.6073311Z 2025-03-04T21:02:11.6073562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6073972Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6074047Z 2025-03-04T21:02:11.6074322Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6075797Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6075884Z 2025-03-04T21:02:11.6076165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6076302Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:02:11.6076366Z 2025-03-04T21:02:11.6076617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6077030Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6077101Z 2025-03-04T21:02:11.6077358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6078872Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6078958Z 2025-03-04T21:02:11.6079238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6079373Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:02:11.6079436Z 2025-03-04T21:02:11.6079684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6080103Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6080174Z 2025-03-04T21:02:11.6080432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6081943Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6082030Z 2025-03-04T21:02:11.6082302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6082453Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:02:11.6082517Z 2025-03-04T21:02:11.6082797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6082936Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:02:11.6083005Z 2025-03-04T21:02:11.6083249Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6083670Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6083735Z 2025-03-04T21:02:11.6084002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6085503Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6085586Z 2025-03-04T21:02:11.6085868Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6085999Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:02:11.6086070Z 2025-03-04T21:02:11.6086313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6086732Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6086797Z 2025-03-04T21:02:11.6087078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6088708Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6089040Z 2025-03-04T21:02:11.6089337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6089471Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:02:11.6089545Z 2025-03-04T21:02:11.6089801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6090240Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6090306Z 2025-03-04T21:02:11.6090589Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6092168Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6092258Z 2025-03-04T21:02:11.6092552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6092700Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:02:11.6092774Z 2025-03-04T21:02:11.6093063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6093212Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:02:11.6093277Z 2025-03-04T21:02:11.6093546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6093969Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6094065Z 2025-03-04T21:02:11.6094384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6095955Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6096052Z 2025-03-04T21:02:11.6096341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6096480Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:02:11.6096545Z 2025-03-04T21:02:11.6096807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6097235Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6097310Z 2025-03-04T21:02:11.6097587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6099142Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6099239Z 2025-03-04T21:02:11.6099526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6099668Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:02:11.6099734Z 2025-03-04T21:02:11.6099993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6100427Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6100501Z 2025-03-04T21:02:11.6100783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6102267Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6102356Z 2025-03-04T21:02:11.6102628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6102778Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:02:11.6102841Z 2025-03-04T21:02:11.6103124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6103261Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:02:11.6103331Z 2025-03-04T21:02:11.6103577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6103986Z x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6104056Z 2025-03-04T21:02:11.6104313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6105819Z x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6105898Z 2025-03-04T21:02:11.6106185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6106327Z out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:02:11.6106391Z 2025-03-04T21:02:11.6106640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6107068Z x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6107158Z 2025-03-04T21:02:11.6107426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6108954Z x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6109039Z 2025-03-04T21:02:11.6109327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6109466Z out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:02:11.6109530Z 2025-03-04T21:02:11.6109787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6110207Z x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6110283Z 2025-03-04T21:02:11.6110547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6112060Z x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6112150Z 2025-03-04T21:02:11.6112423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6112574Z x_93 += out_51; out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T21:02:11.6112638Z 2025-03-04T21:02:11.6112922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6113061Z out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:02:11.6113132Z 2025-03-04T21:02:11.6113377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6113804Z x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6113869Z 2025-03-04T21:02:11.6114134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6115602Z x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6115697Z 2025-03-04T21:02:11.6115981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6116109Z out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T21:02:11.6116181Z 2025-03-04T21:02:11.6116423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6116841Z x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6116907Z 2025-03-04T21:02:11.6117171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6118648Z x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6118737Z 2025-03-04T21:02:11.6119024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6119155Z out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:02:11.6119226Z 2025-03-04T21:02:11.6119471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6119891Z x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6119970Z 2025-03-04T21:02:11.6120236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6121706Z x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6121802Z 2025-03-04T21:02:11.6122081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6122220Z x_99 += out_55; out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T21:02:11.6122290Z 2025-03-04T21:02:11.6122565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6122711Z out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:02:11.6122777Z 2025-03-04T21:02:11.6123031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6123440Z x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6123513Z 2025-03-04T21:02:11.6123770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6125262Z x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6125353Z 2025-03-04T21:02:11.6125631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6125775Z out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T21:02:11.6125841Z 2025-03-04T21:02:11.6126092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6126521Z x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6126594Z 2025-03-04T21:02:11.6126853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6128384Z x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6128474Z 2025-03-04T21:02:11.6128755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6128898Z out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:02:11.6128963Z 2025-03-04T21:02:11.6129218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6129636Z x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6129709Z 2025-03-04T21:02:11.6129970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6131470Z x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6131560Z 2025-03-04T21:02:11.6131832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6131988Z x_105 += out_59; out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T21:02:11.6132051Z 2025-03-04T21:02:11.6132332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6132471Z out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:02:11.6132542Z 2025-03-04T21:02:11.6132801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6133218Z x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6133281Z 2025-03-04T21:02:11.6133549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6135135Z x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6135238Z 2025-03-04T21:02:11.6135549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6135691Z out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T21:02:11.6135765Z 2025-03-04T21:02:11.6136032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6136458Z x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6136524Z 2025-03-04T21:02:11.6136793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6138363Z x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6138450Z 2025-03-04T21:02:11.6138739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6138879Z out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T21:02:11.6138953Z 2025-03-04T21:02:11.6139200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6139642Z x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6139708Z 2025-03-04T21:02:11.6139971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6141454Z x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6141534Z 2025-03-04T21:02:11.6141813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6141961Z x_111 += out_63; out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T21:02:11.6142036Z 2025-03-04T21:02:11.6142347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6142499Z out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T21:02:11.6142564Z 2025-03-04T21:02:11.6142826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6143247Z x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6143322Z 2025-03-04T21:02:11.6143611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6145146Z x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6145239Z 2025-03-04T21:02:11.6145523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6145673Z out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T21:02:11.6145738Z 2025-03-04T21:02:11.6146023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6146460Z x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6146528Z 2025-03-04T21:02:11.6146805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6148340Z x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6148433Z 2025-03-04T21:02:11.6148723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6148872Z out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T21:02:11.6148939Z 2025-03-04T21:02:11.6149201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6149638Z x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6149705Z 2025-03-04T21:02:11.6149980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6151568Z x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6151661Z 2025-03-04T21:02:11.6151956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6152112Z x_117 += out_67; out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T21:02:11.6152186Z 2025-03-04T21:02:11.6152470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6152636Z out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T21:02:11.6152702Z 2025-03-04T21:02:11.6152959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6153372Z x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6153445Z 2025-03-04T21:02:11.6153726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6155260Z x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6155337Z 2025-03-04T21:02:11.6155623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6155767Z out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T21:02:11.6155835Z 2025-03-04T21:02:11.6156092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6156517Z x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6156589Z 2025-03-04T21:02:11.6156846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6158354Z x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6158443Z 2025-03-04T21:02:11.6158727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6158869Z out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T21:02:11.6158934Z 2025-03-04T21:02:11.6159200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6159622Z x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6159692Z 2025-03-04T21:02:11.6159952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6161492Z x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6161564Z 2025-03-04T21:02:11.6161834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6161991Z x_123 += out_71; out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T21:02:11.6162055Z 2025-03-04T21:02:11.6162337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6162475Z out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T21:02:11.6162546Z 2025-03-04T21:02:11.6162789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6163203Z x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6163268Z 2025-03-04T21:02:11.6163551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6165073Z x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6165138Z 2025-03-04T21:02:11.6165445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6165579Z out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T21:02:11.6165653Z 2025-03-04T21:02:11.6165900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6166325Z x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6166410Z 2025-03-04T21:02:11.6166675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6168163Z x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6168229Z 2025-03-04T21:02:11.6168513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6168646Z out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T21:02:11.6168718Z 2025-03-04T21:02:11.6168961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6169382Z x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6169447Z 2025-03-04T21:02:11.6169728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6171229Z x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6171320Z 2025-03-04T21:02:11.6171613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6171766Z x_129 += out_75; out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T21:02:11.6171858Z 2025-03-04T21:02:11.6172141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6172289Z out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T21:02:11.6172355Z 2025-03-04T21:02:11.6172613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6173032Z x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6173125Z 2025-03-04T21:02:11.6173391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6175030Z x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6175118Z 2025-03-04T21:02:11.6175424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6175590Z out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T21:02:11.6175656Z 2025-03-04T21:02:11.6175913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6176339Z x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6176437Z 2025-03-04T21:02:11.6176722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6178264Z x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6178343Z 2025-03-04T21:02:11.6178646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6178793Z out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T21:02:11.6178857Z 2025-03-04T21:02:11.6179122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6179547Z x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6179636Z 2025-03-04T21:02:11.6179915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6181453Z x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6181530Z 2025-03-04T21:02:11.6181812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6181975Z x_135 += out_79; out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T21:02:11.6182039Z 2025-03-04T21:02:11.6182331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6182473Z out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T21:02:11.6182545Z 2025-03-04T21:02:11.6182794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6183236Z x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6183324Z 2025-03-04T21:02:11.6183590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6185145Z x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6185213Z 2025-03-04T21:02:11.6185503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6185646Z out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T21:02:11.6185711Z 2025-03-04T21:02:11.6185967Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6186409Z x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6186482Z 2025-03-04T21:02:11.6186749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6188386Z x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6188472Z 2025-03-04T21:02:11.6188766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6188910Z out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T21:02:11.6188976Z 2025-03-04T21:02:11.6189240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6189716Z x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6189816Z 2025-03-04T21:02:11.6190093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6191619Z x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6191695Z 2025-03-04T21:02:11.6192004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6192162Z x_141 += out_83; out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T21:02:11.6192227Z 2025-03-04T21:02:11.6192536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6192679Z out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T21:02:11.6192779Z 2025-03-04T21:02:11.6193055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6193496Z x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6193564Z 2025-03-04T21:02:11.6193857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6195479Z x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6195558Z 2025-03-04T21:02:11.6195869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6196008Z out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T21:02:11.6196084Z 2025-03-04T21:02:11.6196352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6196828Z x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6196910Z 2025-03-04T21:02:11.6197201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6198798Z x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6198878Z 2025-03-04T21:02:11.6199190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6199328Z out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T21:02:11.6199402Z 2025-03-04T21:02:11.6199669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6200141Z x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6200211Z 2025-03-04T21:02:11.6200505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6202099Z x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6202170Z 2025-03-04T21:02:11.6202470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6202627Z x_147 += out_87; out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T21:02:11.6202703Z 2025-03-04T21:02:11.6203012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6203175Z out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T21:02:11.6203256Z 2025-03-04T21:02:11.6203509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6203932Z x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6204007Z 2025-03-04T21:02:11.6204265Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6205754Z x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6205833Z 2025-03-04T21:02:11.6206110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6206252Z out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T21:02:11.6206330Z 2025-03-04T21:02:11.6206584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6206994Z x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6207067Z 2025-03-04T21:02:11.6207326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6208853Z x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6208931Z 2025-03-04T21:02:11.6209214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6209356Z out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T21:02:11.6209424Z 2025-03-04T21:02:11.6209699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6210137Z x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6210226Z 2025-03-04T21:02:11.6210490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6212027Z x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6212105Z 2025-03-04T21:02:11.6212389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6212544Z x_153 += out_91; out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T21:02:11.6212609Z 2025-03-04T21:02:11.6212896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6213057Z out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T21:02:11.6213133Z 2025-03-04T21:02:11.6213386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6213807Z x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6213872Z 2025-03-04T21:02:11.6214161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6215876Z x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6215950Z 2025-03-04T21:02:11.6216241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6216394Z out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T21:02:11.6216472Z 2025-03-04T21:02:11.6216740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6217172Z x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6217245Z 2025-03-04T21:02:11.6217511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6219073Z x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6219143Z 2025-03-04T21:02:11.6219435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6219596Z out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T21:02:11.6219669Z 2025-03-04T21:02:11.6219920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6220352Z x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6220426Z 2025-03-04T21:02:11.6220689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6222223Z x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6222290Z 2025-03-04T21:02:11.6222576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6222725Z x_159 += out_95; out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T21:02:11.6222800Z 2025-03-04T21:02:11.6223101Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6223265Z out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T21:02:11.6223340Z 2025-03-04T21:02:11.6223591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6224019Z x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6224084Z 2025-03-04T21:02:11.6224355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6225916Z x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6226007Z 2025-03-04T21:02:11.6226301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6226447Z out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T21:02:11.6226522Z 2025-03-04T21:02:11.6226772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6227209Z x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6227276Z 2025-03-04T21:02:11.6227548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6229073Z x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6229147Z 2025-03-04T21:02:11.6229444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6229603Z out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T21:02:11.6229693Z 2025-03-04T21:02:11.6229949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6230389Z x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6230457Z 2025-03-04T21:02:11.6230733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6232268Z x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6232347Z 2025-03-04T21:02:11.6232636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6232810Z x_165 += out_99; out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T21:02:11.6232884Z 2025-03-04T21:02:11.6233171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6233325Z out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T21:02:11.6233390Z 2025-03-04T21:02:11.6233647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6234067Z x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6234142Z 2025-03-04T21:02:11.6234407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6235938Z x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6236012Z 2025-03-04T21:02:11.6236315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6236477Z out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T21:02:11.6236543Z 2025-03-04T21:02:11.6236802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6237238Z x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6237313Z 2025-03-04T21:02:11.6237578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6239115Z x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6239205Z 2025-03-04T21:02:11.6239491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6239636Z out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T21:02:11.6239702Z 2025-03-04T21:02:11.6239956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6240387Z x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6240461Z 2025-03-04T21:02:11.6240724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6242263Z x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6242339Z 2025-03-04T21:02:11.6242634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6242820Z x_171 += out_103; out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T21:02:11.6242888Z 2025-03-04T21:02:11.6243179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6243338Z out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T21:02:11.6243412Z 2025-03-04T21:02:11.6243655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6244072Z x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6244137Z 2025-03-04T21:02:11.6244403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6245934Z x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6246014Z 2025-03-04T21:02:11.6246303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6246438Z out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T21:02:11.6246511Z 2025-03-04T21:02:11.6246759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6247184Z x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6247250Z 2025-03-04T21:02:11.6247517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6249025Z x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6249108Z 2025-03-04T21:02:11.6249392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6249525Z out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T21:02:11.6249597Z 2025-03-04T21:02:11.6249844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6250275Z x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6250341Z 2025-03-04T21:02:11.6250610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6252150Z x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6252238Z 2025-03-04T21:02:11.6252528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6252690Z x_177 += out_107; out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T21:02:11.6252765Z 2025-03-04T21:02:11.6253050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6253203Z out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T21:02:11.6253270Z 2025-03-04T21:02:11.6253529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6253958Z x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6254026Z 2025-03-04T21:02:11.6254408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6256094Z x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6256200Z 2025-03-04T21:02:11.6256517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6256666Z out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T21:02:11.6256733Z 2025-03-04T21:02:11.6256993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6257436Z x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6257506Z 2025-03-04T21:02:11.6257796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6259347Z x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6259444Z 2025-03-04T21:02:11.6259735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6259883Z out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T21:02:11.6259954Z 2025-03-04T21:02:11.6260206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6260644Z x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6260712Z 2025-03-04T21:02:11.6260984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6262543Z x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6262634Z 2025-03-04T21:02:11.6262926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6263087Z x_183 += out_111; out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T21:02:11.6263163Z 2025-03-04T21:02:11.6263449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6263603Z out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T21:02:11.6263668Z 2025-03-04T21:02:11.6263930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6264399Z x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6264473Z 2025-03-04T21:02:11.6264736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6266274Z x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6266364Z 2025-03-04T21:02:11.6266653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6266801Z out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T21:02:11.6266867Z 2025-03-04T21:02:11.6267129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6267567Z x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6267642Z 2025-03-04T21:02:11.6267909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6269489Z x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6269583Z 2025-03-04T21:02:11.6269864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6270002Z out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T21:02:11.6270065Z 2025-03-04T21:02:11.6270314Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6270734Z x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6270805Z 2025-03-04T21:02:11.6271082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6272579Z x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6272671Z 2025-03-04T21:02:11.6272943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6273105Z x_189 += out_115; out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T21:02:11.6273169Z 2025-03-04T21:02:11.6273453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6273593Z out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T21:02:11.6273665Z 2025-03-04T21:02:11.6273913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6274331Z x_190: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6274397Z 2025-03-04T21:02:11.6274662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6276185Z x_191: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_190 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6276267Z 2025-03-04T21:02:11.6276562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6276700Z out_120: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_191); x_191 = None 2025-03-04T21:02:11.6276774Z 2025-03-04T21:02:11.6277034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6277480Z x_192: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_120 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6277546Z 2025-03-04T21:02:11.6277811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6279317Z x_193: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_192 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6279399Z 2025-03-04T21:02:11.6279683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6279816Z out_121: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_193); x_193 = None 2025-03-04T21:02:11.6279886Z 2025-03-04T21:02:11.6280134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6280576Z x_194: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_121 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6280642Z 2025-03-04T21:02:11.6280913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6282424Z x_195: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_194 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6282507Z 2025-03-04T21:02:11.6282767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6283202Z x_196: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_119 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:02:11.6283281Z 2025-03-04T21:02:11.6283545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6285141Z x_197: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_196 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6285232Z 2025-03-04T21:02:11.6285513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6285675Z x_195 += x_197; out_122: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_195; x_195 = x_197 = None 2025-03-04T21:02:11.6285742Z 2025-03-04T21:02:11.6286031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6286179Z out_123: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_122); out_122 = None 2025-03-04T21:02:11.6286264Z 2025-03-04T21:02:11.6286514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6286943Z x_198: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6287011Z 2025-03-04T21:02:11.6287282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6288955Z x_199: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_198 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6289054Z 2025-03-04T21:02:11.6289353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6289493Z out_124: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_199); x_199 = None 2025-03-04T21:02:11.6289569Z 2025-03-04T21:02:11.6289821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6290256Z x_200: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_124 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6290334Z 2025-03-04T21:02:11.6290621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6292132Z x_201: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_200 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6292222Z 2025-03-04T21:02:11.6292523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6292661Z out_125: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_201); x_201 = None 2025-03-04T21:02:11.6292736Z 2025-03-04T21:02:11.6292987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6293428Z x_202: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_125 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6293507Z 2025-03-04T21:02:11.6293774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6295399Z x_203: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_202 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6295500Z 2025-03-04T21:02:11.6295829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6296019Z x_203 += out_123; out_126: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_203; x_203 = out_123 = None 2025-03-04T21:02:11.6296095Z 2025-03-04T21:02:11.6296394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6296559Z out_127: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_126); out_126 = None 2025-03-04T21:02:11.6296628Z 2025-03-04T21:02:11.6296900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6297368Z x_204: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6297439Z 2025-03-04T21:02:11.6297766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6299294Z x_205: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_204 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6299387Z 2025-03-04T21:02:11.6299677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6299822Z out_128: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_205); x_205 = None 2025-03-04T21:02:11.6299893Z 2025-03-04T21:02:11.6300145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6300582Z x_206: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_128 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6300650Z 2025-03-04T21:02:11.6300925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6302472Z x_207: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_206 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6302561Z 2025-03-04T21:02:11.6302857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6302996Z out_129: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_207); x_207 = None 2025-03-04T21:02:11.6303068Z 2025-03-04T21:02:11.6303321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6303761Z x_208: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_129 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6303828Z 2025-03-04T21:02:11.6304120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6305640Z x_209: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_208 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6305753Z 2025-03-04T21:02:11.6306047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6306205Z x_209 += out_127; out_130: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_209; x_209 = out_127 = None 2025-03-04T21:02:11.6306278Z 2025-03-04T21:02:11.6306610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6306765Z out_131: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_130); out_130 = None 2025-03-04T21:02:11.6306832Z 2025-03-04T21:02:11.6307286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:02:11.6307446Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:02:11.6307519Z 2025-03-04T21:02:11.6307820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:11.6307970Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:02:11.6308036Z 2025-03-04T21:02:11.6308483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:02:11.6308652Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:02:11.6308747Z 2025-03-04T21:02:11.6309047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:11.6309194Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:02:11.6309260Z 2025-03-04T21:02:11.6309647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:02:11.6309832Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:02:11.6309941Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:02:11.6310066Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:02:11.6310140Z 2025-03-04T21:02:11.6310480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:02:11.6310632Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:02:11.6310699Z 2025-03-04T21:02:11.6311037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:02:11.6311161Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:02:11.6311235Z 2025-03-04T21:02:11.6311617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:02:11.6311861Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:02:11.6311928Z 2025-03-04T21:02:11.6312358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:02:11.6312486Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:02:11.6312923Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:02:11.6313056Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:02:11.6313173Z x_210: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:02:11.6313247Z 2025-03-04T21:02:11.6313549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:02:11.6313687Z tensor: "f32[82125, 4][4, 1]cpu" = x_210.to(torch.float32); x_210 = None 2025-03-04T21:02:11.6313752Z 2025-03-04T21:02:11.6314013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6314793Z x_211: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_131, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_131 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:02:11.6314869Z 2025-03-04T21:02:11.6315163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:02:11.6315382Z x_212: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_211, inplace = False); x_211 = None 2025-03-04T21:02:11.6315450Z 2025-03-04T21:02:11.6315840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:02:11.6316685Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:02:11.6316761Z 2025-03-04T21:02:11.6317145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:02:11.6317965Z x_213: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_212 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:02:11.6318064Z 2025-03-04T21:02:11.6318407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:02:11.6318571Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:02:11.6318711Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:02:11.6318786Z 2025-03-04T21:02:11.6319204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:02:11.6319370Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_213.view(4, -1, 4, 73, 75); x_213 = None 2025-03-04T21:02:11.6319541Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:02:11.6319728Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:02:11.6319794Z 2025-03-04T21:02:11.6320206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:02:11.6320421Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:02:11.6320486Z 2025-03-04T21:02:11.6320922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:02:11.6321071Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:02:11.6321226Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:02:11.6321382Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:02:11.6321468Z 2025-03-04T21:02:11.6321840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:02:11.6322017Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:02:11.6322083Z 2025-03-04T21:02:11.6322400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:02:11.6322540Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:02:11.6322611Z 2025-03-04T21:02:11.6322927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:02:11.6323065Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:02:11.6323194Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:11.6323363Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:02:11.6323429Z 2025-03-04T21:02:11.6323765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:02:11.6323887Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:02:11.6324010Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:02:11.6324154Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:02:11.6324243Z 2025-03-04T21:02:11.6324551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:02:11.6324681Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:11.6324771Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:02:11.6324900Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:02:11.6324964Z 2025-03-04T21:02:11.6325275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:02:11.6325416Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:02:11.6325514Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:02:11.6325643Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:02:11.6325715Z 2025-03-04T21:02:11.6326076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:02:11.6326237Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:02:11.6326351Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:02:11.6326425Z 2025-03-04T21:02:11.6329940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:02:11.6330108Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:02:11.6330227Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:02:11.6330309Z 2025-03-04T21:02:11.6330652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:02:11.6330858Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:02:11.6330983Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:02:11.6331051Z 2025-03-04T21:02:11.6331360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:02:11.6331545Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:02:11.6331663Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:02:11.6331729Z 2025-03-04T21:02:11.6332071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:02:11.6332216Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:02:11.6332289Z 2025-03-04T21:02:11.6332638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:02:11.6332785Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:02:11.6332851Z 2025-03-04T21:02:11.6333209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:02:11.6333349Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:02:11.6333503Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:02:11.6333661Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:02:11.6333810Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:02:11.6333875Z 2025-03-04T21:02:11.6334315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:02:11.6334466Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:02:11.6334603Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:02:11.6334758Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:02:11.6334907Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:02:11.6334976Z 2025-03-04T21:02:11.6335320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:02:11.6335445Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:02:11.6335622Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:02:11.6335753Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:02:11.6335829Z 2025-03-04T21:02:11.6336161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:02:11.6336287Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:02:11.6336454Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:02:11.6336615Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:02:11.6336696Z 2025-03-04T21:02:11.6337024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:02:11.6337122Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:02:11.6337246Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:02:11.6337311Z 2025-03-04T21:02:11.6337627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:02:11.6337720Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:02:11.6337841Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:02:11.6337907Z 2025-03-04T21:02:11.6338221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:02:11.6338336Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:02:11.6338485Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:02:11.6338552Z 2025-03-04T21:02:11.6338866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:02:11.6338983Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:02:11.6339111Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:02:11.6339182Z 2025-03-04T21:02:11.6339533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:02:11.6339742Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:02:11.6339809Z 2025-03-04T21:02:11.6340149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:02:11.6340310Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:02:11.6340384Z 2025-03-04T21:02:11.6340773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:02:11.6340955Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:02:11.6341026Z 2025-03-04T21:02:11.6341520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:02:11.6341661Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:02:11.6341735Z 2025-03-04T21:02:11.6342036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:11.6342185Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:02:11.6342254Z 2025-03-04T21:02:11.6342699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:02:11.6342835Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:02:11.6342949Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:02:11.6343094Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:02:11.6343169Z 2025-03-04T21:02:11.6343629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:02:11.6343799Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:02:11.6344035Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:02:11.6344108Z 2025-03-04T21:02:11.6344568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:02:11.6344748Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:02:11.6345043Z 2025-03-04T21:02:11.6345354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:11.6345510Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:02:11.6345585Z 2025-03-04T21:02:11.6345970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:02:11.6346146Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:02:11.6346219Z 2025-03-04T21:02:11.6346519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:02:11.6346673Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:02:11.6346739Z 2025-03-04T21:02:11.6347123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:02:11.6347264Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:02:11.6347338Z 2025-03-04T21:02:11.6347817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:02:11.6347969Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:02:11.6348092Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:02:11.6348259Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:02:11.6348393Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:02:11.6348464Z 2025-03-04T21:02:11.6348833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:02:11.6348959Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:02:11.6349026Z 2025-03-04T21:02:11.6349035Z 2025-03-04T21:02:11.6349141Z class GraphModule(torch.nn.Module): 2025-03-04T21:02:11.6449804Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: 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L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:02:11.6450956Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:02:11.6451310Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6451717Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6452138Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6452529Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6452889Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6453249Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6453699Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6454109Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6454544Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6454941Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6455291Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6455724Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6456139Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6456531Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6456920Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6457238Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6457705Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6458147Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6458541Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6458923Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6459298Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:02:11.6459735Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6460192Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6460599Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6460941Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6461241Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6461585Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6461918Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6462238Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6462542Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6462841Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6463216Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6463577Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6463898Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6464201Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6464504Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6464857Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6465198Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6465513Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6465874Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6466189Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6466592Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6466982Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6467344Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6467701Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6468031Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6468415Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6468791Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6469157Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6469491Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6469811Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6470190Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6470564Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6470925Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6471293Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6471613Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6472010Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6472373Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6472689Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6473011Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6473299Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6473651Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6473989Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6474302Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6474635Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6474920Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6475265Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6475597Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6475917Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6476233Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6476540Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:02:11.6476913Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6477258Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6477595Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6477949Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6478254Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6478592Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6478931Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6479250Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6479559Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6479865Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6480201Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6480539Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6480852Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6481197Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6481483Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6481825Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6482165Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6482480Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6482791Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6483069Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6483407Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6483736Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6484078Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6484426Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6484717Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6485064Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6485397Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6485719Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6486044Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6486334Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6486666Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6487004Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6487336Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6487654Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6487971Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6488463Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6488820Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6489155Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6489487Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6489782Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6490143Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6490490Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6490863Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6491247Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6491550Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6491917Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6492275Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6492642Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6493020Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6493346Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6493728Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6494127Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6494570Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6494925Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6495254Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6495653Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6496022Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6496374Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6496707Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6497004Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6497371Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6497763Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6498123Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6498462Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6498786Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:02:11.6499186Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6499566Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6499957Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6500302Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6500610Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6500970Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6501342Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6501686Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6502007Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6502314Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6502667Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6503028Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6503360Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6503691Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6503994Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6504368Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6504728Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6505080Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6505400Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6505683Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6506036Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6506398Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6506730Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6507053Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6507340Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6507708Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6508050Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6508380Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6508693Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6508991Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6509337Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6509691Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6510020Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6510335Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6510629Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6510990Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6511355Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6511675Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6511996Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6512286Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6512643Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6513010Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6513338Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6513656Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6513943Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6514317Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6514662Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6514993Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6515314Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6515614Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6515967Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6516311Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6516642Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6516958Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6517275Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6517624Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6517992Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6518316Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6518638Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6518938Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6519301Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6519646Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6519967Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6520289Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6520593Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6520950Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6521289Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6521618Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6521940Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6522229Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6522581Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6522920Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6523249Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6523562Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6523877Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6524238Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6524588Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6524919Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6525231Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6525531Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6525896Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6526244Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6526568Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6526881Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6527182Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6527529Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6527878Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6528197Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6528520Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6528810Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6529162Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6529498Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6529825Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6530154Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6530481Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6530835Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6531174Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6531503Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6531822Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6532135Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6532480Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6532829Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6533152Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6533499Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6533812Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6534175Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6534636Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6535010Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6535381Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6535710Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6536111Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6536471Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6536799Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6537146Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6537457Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6537808Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6538147Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6538477Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6538793Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6539110Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6539455Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6539797Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6540144Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6540458Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6540751Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6541092Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6541435Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6541756Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6542082Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6542367Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6542713Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6543062Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6543401Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6543742Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6544031Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6544380Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6544718Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6545052Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6545383Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6545686Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6546042Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6546384Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6546740Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6547062Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6547360Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6547706Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6548061Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6548387Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6548710Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6549006Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6549353Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6549717Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6550043Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6550384Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6550678Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6551031Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6551377Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6551722Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6552047Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6552338Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6552689Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6553047Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6553384Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6553703Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6554007Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6554355Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6554711Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6555057Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6555376Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6555677Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6556071Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6556448Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6556788Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6557113Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6557400Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6557757Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6558110Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6558457Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6558781Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6559072Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6559428Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6559796Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6560134Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6560448Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6560745Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6561102Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6561452Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6561786Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6562101Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6562402Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6562772Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6563147Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6563471Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6563796Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6564090Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6564443Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6564816Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6565142Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6565468Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6565758Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6566136Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6566486Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6566818Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6567142Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6567433Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6567786Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6568129Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6568458Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6568770Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6569086Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6569450Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6569799Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6570129Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6570443Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6570741Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6571106Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6571457Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6571780Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6572100Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6572406Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6572758Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6573104Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6573425Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6573744Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6574037Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6574460Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6574814Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6575176Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6575523Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6575856Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6576249Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6576616Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6576948Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6577280Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6577598Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6577984Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6578361Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6578711Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6579072Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6579386Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6579755Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6580122Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6580461Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6580803Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6581116Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6581487Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6581853Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6582194Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6582562Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6582887Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6583260Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6583623Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6583974Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6584312Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6584649Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6585009Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6585352Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6585687Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6586018Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6586321Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6586670Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6587022Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6587348Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6587686Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6587984Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6588508Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6588863Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6589238Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6589605Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6589908Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6590278Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6590635Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6590988Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6591347Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6591656Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6592019Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6592358Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6592718Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6593036Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6593336Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6593680Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6594032Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6594367Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6594684Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6594983Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6595330Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6595722Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6596062Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6596386Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6596671Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6597022Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6597377Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6597715Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6598037Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6598326Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6598678Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6599043Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6599375Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6599687Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6599986Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6600334Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6600679Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6601009Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6601320Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6601614Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6601956Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6602321Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6602663Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6602985Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6603283Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6603629Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6603980Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6604333Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6604655Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6604942Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6605318Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6605664Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6605996Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6606320Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6606612Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6606969Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6607316Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6607648Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6607961Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6608260Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6608636Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6609258Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6609613Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6609961Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6610289Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6610661Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6611064Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6611411Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6611756Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6612064Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6612470Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6612843Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6613185Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6613538Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6613850Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6614298Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6614691Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6615060Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6615419Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6615755Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6616146Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6616507Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6616854Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6617188Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6617522Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:02:11.6617916Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6618300Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6618653Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6619008Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6619340Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6619707Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6620072Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6620411Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6620749Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6621057Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6621430Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6621790Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6622140Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6622480Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6622801Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6623189Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6623557Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6623888Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6624207Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6624502Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:02:11.6624863Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6625208Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6625535Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6625871Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6626170Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:02:11.6626515Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6626861Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6627184Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6627511Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6627806Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:02:11.6628161Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:02:11.6628517Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:02:11.6628848Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:02:11.6629188Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:02:11.6629564Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:02:11.6629891Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:02:11.6630202Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:02:11.6630580Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:02:11.6630944Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:02:11.6631323Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:02:11.6631677Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:02:11.6631752Z 2025-03-04T21:02:11.6632054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6632549Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6632639Z 2025-03-04T21:02:11.6632926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6634430Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6634510Z 2025-03-04T21:02:11.6634801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:02:11.6634954Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:02:11.6635019Z 2025-03-04T21:02:11.6635395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:02:11.6635636Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:02:11.6635711Z 2025-03-04T21:02:11.6635986Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6636441Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6636508Z 2025-03-04T21:02:11.6636785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6638365Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6638432Z 2025-03-04T21:02:11.6638726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6638866Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:02:11.6638939Z 2025-03-04T21:02:11.6639211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6639669Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6639734Z 2025-03-04T21:02:11.6640006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6641540Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6641610Z 2025-03-04T21:02:11.6641909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6642052Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:02:11.6642126Z 2025-03-04T21:02:11.6642380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6642848Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6642933Z 2025-03-04T21:02:11.6643210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6644748Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6644818Z 2025-03-04T21:02:11.6645078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6645517Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:02:11.6645590Z 2025-03-04T21:02:11.6645871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6647457Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6647533Z 2025-03-04T21:02:11.6647816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6647973Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:02:11.6648040Z 2025-03-04T21:02:11.6648334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6648485Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:02:11.6648559Z 2025-03-04T21:02:11.6648808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6649260Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6649344Z 2025-03-04T21:02:11.6649623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6651143Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6651213Z 2025-03-04T21:02:11.6651526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6651677Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:02:11.6651751Z 2025-03-04T21:02:11.6652004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6652447Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6652530Z 2025-03-04T21:02:11.6652808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6654440Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6654525Z 2025-03-04T21:02:11.6654864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6655025Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:02:11.6655104Z 2025-03-04T21:02:11.6655388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6655882Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6655967Z 2025-03-04T21:02:11.6656264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6657825Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6657892Z 2025-03-04T21:02:11.6658182Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6658357Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:02:11.6658433Z 2025-03-04T21:02:11.6658714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6658870Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:02:11.6658935Z 2025-03-04T21:02:11.6659194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6659643Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6659710Z 2025-03-04T21:02:11.6659984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6661521Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6661597Z 2025-03-04T21:02:11.6661884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6662033Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:02:11.6662098Z 2025-03-04T21:02:11.6662359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6662804Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6662889Z 2025-03-04T21:02:11.6663162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6664697Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6664770Z 2025-03-04T21:02:11.6665070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6665221Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:02:11.6665289Z 2025-03-04T21:02:11.6665547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6665997Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6666082Z 2025-03-04T21:02:11.6666355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6667878Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6667956Z 2025-03-04T21:02:11.6668247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6668405Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:02:11.6668482Z 2025-03-04T21:02:11.6668769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6668936Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:02:11.6669007Z 2025-03-04T21:02:11.6669270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6669715Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6669803Z 2025-03-04T21:02:11.6670071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6671622Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6671698Z 2025-03-04T21:02:11.6671982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6672133Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:02:11.6672198Z 2025-03-04T21:02:11.6672458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6672907Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6672982Z 2025-03-04T21:02:11.6673248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6674778Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6674855Z 2025-03-04T21:02:11.6675142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6675292Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:02:11.6675358Z 2025-03-04T21:02:11.6675616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6676069Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6676157Z 2025-03-04T21:02:11.6676425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6677958Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6678055Z 2025-03-04T21:02:11.6678313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6678775Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:02:11.6678841Z 2025-03-04T21:02:11.6679113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6680736Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6680813Z 2025-03-04T21:02:11.6681104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6681256Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:02:11.6681330Z 2025-03-04T21:02:11.6681614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6681775Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:02:11.6681841Z 2025-03-04T21:02:11.6682095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6682534Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6682625Z 2025-03-04T21:02:11.6682893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6684421Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6684496Z 2025-03-04T21:02:11.6684797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6684947Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:02:11.6685011Z 2025-03-04T21:02:11.6685266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6685695Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6685785Z 2025-03-04T21:02:11.6686051Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6687602Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6687675Z 2025-03-04T21:02:11.6687965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6688219Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:02:11.6688293Z 2025-03-04T21:02:11.6688556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6688995Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6689074Z 2025-03-04T21:02:11.6689385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6690927Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6691003Z 2025-03-04T21:02:11.6691282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6691471Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:02:11.6691541Z 2025-03-04T21:02:11.6691861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6692023Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:02:11.6692101Z 2025-03-04T21:02:11.6692375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6692839Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6692907Z 2025-03-04T21:02:11.6693187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6694853Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6694935Z 2025-03-04T21:02:11.6695275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6695434Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:02:11.6695515Z 2025-03-04T21:02:11.6695804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6696309Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6696399Z 2025-03-04T21:02:11.6696699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6698384Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6698461Z 2025-03-04T21:02:11.6698820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6698980Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:02:11.6699062Z 2025-03-04T21:02:11.6699352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6699857Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6699982Z 2025-03-04T21:02:11.6700281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6701974Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6702048Z 2025-03-04T21:02:11.6702388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6702561Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:02:11.6702642Z 2025-03-04T21:02:11.6702957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6703132Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:02:11.6703203Z 2025-03-04T21:02:11.6703491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6704000Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6704094Z 2025-03-04T21:02:11.6704368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6705927Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6706006Z 2025-03-04T21:02:11.6706291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6706442Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:02:11.6706506Z 2025-03-04T21:02:11.6706760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6707210Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6707279Z 2025-03-04T21:02:11.6707550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6709068Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6709144Z 2025-03-04T21:02:11.6709435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6709576Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:02:11.6709649Z 2025-03-04T21:02:11.6709900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6710354Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6710438Z 2025-03-04T21:02:11.6710713Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6712245Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6712335Z 2025-03-04T21:02:11.6712621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6712778Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:02:11.6712851Z 2025-03-04T21:02:11.6713132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6713288Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:02:11.6713369Z 2025-03-04T21:02:11.6713631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6714051Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6714123Z 2025-03-04T21:02:11.6714387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6715915Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6715987Z 2025-03-04T21:02:11.6716273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6716421Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:02:11.6716485Z 2025-03-04T21:02:11.6716762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6717189Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6717275Z 2025-03-04T21:02:11.6717544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6719084Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6719160Z 2025-03-04T21:02:11.6719444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6719586Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:02:11.6719652Z 2025-03-04T21:02:11.6719908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6720356Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6720431Z 2025-03-04T21:02:11.6720702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6722228Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6722303Z 2025-03-04T21:02:11.6722556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6722998Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:02:11.6723065Z 2025-03-04T21:02:11.6723353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6724901Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6724994Z 2025-03-04T21:02:11.6725285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6725444Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:02:11.6725518Z 2025-03-04T21:02:11.6725803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6725957Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:02:11.6726024Z 2025-03-04T21:02:11.6726289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6726716Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6726806Z 2025-03-04T21:02:11.6727072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6728605Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6728680Z 2025-03-04T21:02:11.6728968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6729112Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:02:11.6729177Z 2025-03-04T21:02:11.6729435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6729877Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6729953Z 2025-03-04T21:02:11.6730243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6731767Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6731840Z 2025-03-04T21:02:11.6732140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6732284Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:02:11.6732351Z 2025-03-04T21:02:11.6732610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6733038Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6733131Z 2025-03-04T21:02:11.6733400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6735074Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6735163Z 2025-03-04T21:02:11.6735481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6735664Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:02:11.6735729Z 2025-03-04T21:02:11.6736022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6736168Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:02:11.6736249Z 2025-03-04T21:02:11.6736531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6737029Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6737120Z 2025-03-04T21:02:11.6737426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6739127Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6739205Z 2025-03-04T21:02:11.6739535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6739688Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:02:11.6739770Z 2025-03-04T21:02:11.6740052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6740541Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6740656Z 2025-03-04T21:02:11.6740969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6742676Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6742752Z 2025-03-04T21:02:11.6743081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6743234Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:02:11.6743307Z 2025-03-04T21:02:11.6743563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6744014Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6744083Z 2025-03-04T21:02:11.6744370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6745916Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6745983Z 2025-03-04T21:02:11.6746286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6746435Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:02:11.6746510Z 2025-03-04T21:02:11.6746796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6746947Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:02:11.6747014Z 2025-03-04T21:02:11.6747277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6747718Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6747786Z 2025-03-04T21:02:11.6748059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6749603Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6749677Z 2025-03-04T21:02:11.6749963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6750108Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:02:11.6750172Z 2025-03-04T21:02:11.6750431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6750880Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6750961Z 2025-03-04T21:02:11.6751239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6752781Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6752858Z 2025-03-04T21:02:11.6753140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6753280Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:02:11.6753345Z 2025-03-04T21:02:11.6753603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6754033Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6754117Z 2025-03-04T21:02:11.6754397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6755915Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6755991Z 2025-03-04T21:02:11.6756272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6756426Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:02:11.6756497Z 2025-03-04T21:02:11.6756783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6756935Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:02:11.6757001Z 2025-03-04T21:02:11.6757285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6757715Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6757788Z 2025-03-04T21:02:11.6758056Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6759597Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6759675Z 2025-03-04T21:02:11.6759962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6760106Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:02:11.6760171Z 2025-03-04T21:02:11.6760431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6760872Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6760946Z 2025-03-04T21:02:11.6761209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6762701Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6762776Z 2025-03-04T21:02:11.6763054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6763194Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:02:11.6763256Z 2025-03-04T21:02:11.6763510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6763940Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6764037Z 2025-03-04T21:02:11.6764300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6765811Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6765885Z 2025-03-04T21:02:11.6766160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6766316Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:02:11.6766384Z 2025-03-04T21:02:11.6766667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6766806Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:02:11.6766893Z 2025-03-04T21:02:11.6767142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6767554Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6767621Z 2025-03-04T21:02:11.6767893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6769408Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6769488Z 2025-03-04T21:02:11.6769785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6769920Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:02:11.6769993Z 2025-03-04T21:02:11.6770261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6770708Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6770774Z 2025-03-04T21:02:11.6771045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6772587Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6772664Z 2025-03-04T21:02:11.6772961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6773093Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:02:11.6773165Z 2025-03-04T21:02:11.6773415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6773866Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6773935Z 2025-03-04T21:02:11.6774272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6775891Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6775958Z 2025-03-04T21:02:11.6776243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6776388Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:02:11.6776459Z 2025-03-04T21:02:11.6776744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6776914Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:02:11.6776982Z 2025-03-04T21:02:11.6777256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6777671Z x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6777745Z 2025-03-04T21:02:11.6778009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6779533Z x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6779612Z 2025-03-04T21:02:11.6779904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6780047Z out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:02:11.6780138Z 2025-03-04T21:02:11.6780400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6780828Z x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6780904Z 2025-03-04T21:02:11.6781172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6782689Z x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6782765Z 2025-03-04T21:02:11.6783050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6783195Z out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:02:11.6783262Z 2025-03-04T21:02:11.6783540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6784006Z x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6784082Z 2025-03-04T21:02:11.6784360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6785910Z x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6785989Z 2025-03-04T21:02:11.6786267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6786419Z x_93 += out_51; out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T21:02:11.6786484Z 2025-03-04T21:02:11.6786777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6786939Z out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:02:11.6787014Z 2025-03-04T21:02:11.6787271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6787698Z x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6787763Z 2025-03-04T21:02:11.6788037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6789673Z x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6789749Z 2025-03-04T21:02:11.6790043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6790226Z out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T21:02:11.6790303Z 2025-03-04T21:02:11.6790581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6791014Z x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6791081Z 2025-03-04T21:02:11.6791356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6792897Z x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6792968Z 2025-03-04T21:02:11.6793264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6793397Z out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:02:11.6793496Z 2025-03-04T21:02:11.6793749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6794186Z x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6794261Z 2025-03-04T21:02:11.6794527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6796047Z x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6796118Z 2025-03-04T21:02:11.6796401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6796544Z x_99 += out_55; out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T21:02:11.6796618Z 2025-03-04T21:02:11.6796920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6797087Z out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:02:11.6797153Z 2025-03-04T21:02:11.6797415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6797842Z x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6797908Z 2025-03-04T21:02:11.6798179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6799724Z x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6799801Z 2025-03-04T21:02:11.6800086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6800256Z out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T21:02:11.6800323Z 2025-03-04T21:02:11.6800584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6801023Z x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6801088Z 2025-03-04T21:02:11.6801368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6802903Z x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6802979Z 2025-03-04T21:02:11.6803282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6803443Z out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:02:11.6803517Z 2025-03-04T21:02:11.6803787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6804218Z x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6804283Z 2025-03-04T21:02:11.6804555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6806132Z x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6806214Z 2025-03-04T21:02:11.6806507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6806659Z x_105 += out_59; out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T21:02:11.6806747Z 2025-03-04T21:02:11.6807025Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6807174Z out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:02:11.6807238Z 2025-03-04T21:02:11.6807489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6807905Z x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6807977Z 2025-03-04T21:02:11.6808245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6809788Z x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6809866Z 2025-03-04T21:02:11.6810168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6810335Z out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T21:02:11.6810402Z 2025-03-04T21:02:11.6810661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6811084Z x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6811156Z 2025-03-04T21:02:11.6811419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6812964Z x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6813043Z 2025-03-04T21:02:11.6813349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6813513Z out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T21:02:11.6813580Z 2025-03-04T21:02:11.6813840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6814303Z x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6814384Z 2025-03-04T21:02:11.6814656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6816343Z x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6816421Z 2025-03-04T21:02:11.6816720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6816901Z x_111 += out_63; out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T21:02:11.6816981Z 2025-03-04T21:02:11.6817279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6817421Z out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T21:02:11.6817493Z 2025-03-04T21:02:11.6817748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6818177Z x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6818245Z 2025-03-04T21:02:11.6818520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6820073Z x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6820156Z 2025-03-04T21:02:11.6820448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6820592Z out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T21:02:11.6820665Z 2025-03-04T21:02:11.6820917Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6821356Z x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6821425Z 2025-03-04T21:02:11.6821702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6823224Z x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6823293Z 2025-03-04T21:02:11.6823604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6823758Z out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T21:02:11.6823831Z 2025-03-04T21:02:11.6824083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6824517Z x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6824584Z 2025-03-04T21:02:11.6824853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6826387Z x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6826472Z 2025-03-04T21:02:11.6826762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6826916Z x_117 += out_67; out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T21:02:11.6826992Z 2025-03-04T21:02:11.6827275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6827427Z out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T21:02:11.6827492Z 2025-03-04T21:02:11.6827755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6828174Z x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6828248Z 2025-03-04T21:02:11.6828515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6830086Z x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6830183Z 2025-03-04T21:02:11.6830472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6830619Z out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T21:02:11.6830686Z 2025-03-04T21:02:11.6830949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6831378Z x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6831453Z 2025-03-04T21:02:11.6831722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6833297Z x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6833393Z 2025-03-04T21:02:11.6833682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6833827Z out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T21:02:11.6833892Z 2025-03-04T21:02:11.6834150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6834588Z x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6834664Z 2025-03-04T21:02:11.6834940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6836471Z x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6836567Z 2025-03-04T21:02:11.6836852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6837030Z x_123 += out_71; out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T21:02:11.6837096Z 2025-03-04T21:02:11.6837388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6837530Z out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T21:02:11.6837604Z 2025-03-04T21:02:11.6837856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6838282Z x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6838357Z 2025-03-04T21:02:11.6838641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6840185Z x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6840269Z 2025-03-04T21:02:11.6840557Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6840699Z out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T21:02:11.6840765Z 2025-03-04T21:02:11.6841022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6841439Z x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6841513Z 2025-03-04T21:02:11.6841772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6843284Z x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6843373Z 2025-03-04T21:02:11.6843652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6843793Z out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T21:02:11.6843858Z 2025-03-04T21:02:11.6844112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6844532Z x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6844608Z 2025-03-04T21:02:11.6844868Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6846378Z x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6846469Z 2025-03-04T21:02:11.6846745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6846904Z x_129 += out_75; out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T21:02:11.6846970Z 2025-03-04T21:02:11.6847257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6847400Z out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T21:02:11.6847473Z 2025-03-04T21:02:11.6847725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6848155Z x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6848223Z 2025-03-04T21:02:11.6848497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6850023Z x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6850114Z 2025-03-04T21:02:11.6850404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6850540Z out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T21:02:11.6850613Z 2025-03-04T21:02:11.6850864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6851294Z x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6851376Z 2025-03-04T21:02:11.6851652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6853216Z x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6853311Z 2025-03-04T21:02:11.6853617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6853759Z out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T21:02:11.6853832Z 2025-03-04T21:02:11.6854093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6854642Z x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6854723Z 2025-03-04T21:02:11.6855025Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6856627Z x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6856715Z 2025-03-04T21:02:11.6857007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6857160Z x_135 += out_79; out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T21:02:11.6857241Z 2025-03-04T21:02:11.6857567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6857737Z out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T21:02:11.6857812Z 2025-03-04T21:02:11.6858111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6858598Z x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6858684Z 2025-03-04T21:02:11.6858987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6860665Z x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6860767Z 2025-03-04T21:02:11.6861094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6861254Z out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T21:02:11.6861329Z 2025-03-04T21:02:11.6861630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6862112Z x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6862197Z 2025-03-04T21:02:11.6862501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6864255Z x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6864350Z 2025-03-04T21:02:11.6864633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6864777Z out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T21:02:11.6864843Z 2025-03-04T21:02:11.6865102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6865551Z x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6865628Z 2025-03-04T21:02:11.6865891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6867421Z x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6867514Z 2025-03-04T21:02:11.6867792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6867951Z x_141 += out_83; out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T21:02:11.6868019Z 2025-03-04T21:02:11.6868308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6868454Z out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T21:02:11.6868529Z 2025-03-04T21:02:11.6868781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6869205Z x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6869270Z 2025-03-04T21:02:11.6869547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6871102Z x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6871186Z 2025-03-04T21:02:11.6871480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6871618Z out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T21:02:11.6871693Z 2025-03-04T21:02:11.6871944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6872390Z x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6872461Z 2025-03-04T21:02:11.6872724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6874313Z x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6874396Z 2025-03-04T21:02:11.6874687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6874823Z out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T21:02:11.6874898Z 2025-03-04T21:02:11.6875149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6875583Z x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6875657Z 2025-03-04T21:02:11.6875922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6877461Z x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6877544Z 2025-03-04T21:02:11.6877832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6877990Z x_147 += out_87; out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T21:02:11.6878057Z 2025-03-04T21:02:11.6878350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6878494Z out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T21:02:11.6878566Z 2025-03-04T21:02:11.6878835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6879264Z x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6879331Z 2025-03-04T21:02:11.6879602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6881161Z x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6881237Z 2025-03-04T21:02:11.6881531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6881673Z out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T21:02:11.6881747Z 2025-03-04T21:02:11.6882001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6882441Z x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6882505Z 2025-03-04T21:02:11.6882779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6884326Z x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6884418Z 2025-03-04T21:02:11.6884709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6884848Z out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T21:02:11.6884923Z 2025-03-04T21:02:11.6885174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6885648Z x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6885715Z 2025-03-04T21:02:11.6885986Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6887517Z x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6887607Z 2025-03-04T21:02:11.6887893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6888045Z x_153 += out_91; out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T21:02:11.6888227Z 2025-03-04T21:02:11.6888525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6888680Z out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T21:02:11.6888748Z 2025-03-04T21:02:11.6889011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6889434Z x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6889512Z 2025-03-04T21:02:11.6889817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6891338Z x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6891439Z 2025-03-04T21:02:11.6891734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6891900Z out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T21:02:11.6891999Z 2025-03-04T21:02:11.6892291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6892762Z x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6892844Z 2025-03-04T21:02:11.6893140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6894956Z x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6895047Z 2025-03-04T21:02:11.6895383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6895562Z out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T21:02:11.6895637Z 2025-03-04T21:02:11.6895932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6896410Z x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6896494Z 2025-03-04T21:02:11.6896789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6898539Z x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6898640Z 2025-03-04T21:02:11.6898952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6899133Z x_159 += out_95; out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T21:02:11.6899208Z 2025-03-04T21:02:11.6899547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6899710Z out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T21:02:11.6899790Z 2025-03-04T21:02:11.6900073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6900544Z x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6900636Z 2025-03-04T21:02:11.6900942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6902594Z x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6902662Z 2025-03-04T21:02:11.6902956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6903098Z out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T21:02:11.6903171Z 2025-03-04T21:02:11.6903424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6903860Z x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6903928Z 2025-03-04T21:02:11.6904237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6905768Z x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6905836Z 2025-03-04T21:02:11.6906131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6906298Z out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T21:02:11.6906373Z 2025-03-04T21:02:11.6906624Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6907071Z x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6907137Z 2025-03-04T21:02:11.6907425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6908954Z x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6909024Z 2025-03-04T21:02:11.6909309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6909464Z x_165 += out_99; out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T21:02:11.6909538Z 2025-03-04T21:02:11.6909819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6909972Z out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T21:02:11.6910036Z 2025-03-04T21:02:11.6910293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6910734Z x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6910840Z 2025-03-04T21:02:11.6911114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6912645Z x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6912719Z 2025-03-04T21:02:11.6913018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6913166Z out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T21:02:11.6913231Z 2025-03-04T21:02:11.6913491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6913940Z x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6914025Z 2025-03-04T21:02:11.6914299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6915830Z x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6915908Z 2025-03-04T21:02:11.6916194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6916337Z out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T21:02:11.6916409Z 2025-03-04T21:02:11.6916662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6917107Z x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6917174Z 2025-03-04T21:02:11.6917457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6919002Z x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6919080Z 2025-03-04T21:02:11.6919384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6919548Z x_171 += out_103; out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T21:02:11.6919624Z 2025-03-04T21:02:11.6919911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6920068Z out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T21:02:11.6920134Z 2025-03-04T21:02:11.6920398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6920842Z x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6920919Z 2025-03-04T21:02:11.6921197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6922698Z x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6922773Z 2025-03-04T21:02:11.6923051Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6923192Z out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T21:02:11.6923255Z 2025-03-04T21:02:11.6923509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6923949Z x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6924037Z 2025-03-04T21:02:11.6924295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6925792Z x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6925882Z 2025-03-04T21:02:11.6926160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6926302Z out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T21:02:11.6926366Z 2025-03-04T21:02:11.6926619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6927039Z x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6927129Z 2025-03-04T21:02:11.6927389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6928935Z x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6929009Z 2025-03-04T21:02:11.6929288Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6929453Z x_177 += out_107; out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T21:02:11.6929520Z 2025-03-04T21:02:11.6929808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6929952Z out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T21:02:11.6930025Z 2025-03-04T21:02:11.6930292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6930750Z x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6930816Z 2025-03-04T21:02:11.6931091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6932664Z x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6932736Z 2025-03-04T21:02:11.6933034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6933173Z out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T21:02:11.6933247Z 2025-03-04T21:02:11.6933514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6933959Z x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6934035Z 2025-03-04T21:02:11.6934401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6936043Z x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6936112Z 2025-03-04T21:02:11.6936400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6936537Z out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T21:02:11.6936610Z 2025-03-04T21:02:11.6936871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6937356Z x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6937441Z 2025-03-04T21:02:11.6937730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6939374Z x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6939449Z 2025-03-04T21:02:11.6939752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6939916Z x_183 += out_111; out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T21:02:11.6939994Z 2025-03-04T21:02:11.6940292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6940476Z out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T21:02:11.6940544Z 2025-03-04T21:02:11.6940821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6941267Z x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6941342Z 2025-03-04T21:02:11.6941618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6943256Z x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6943337Z 2025-03-04T21:02:11.6943636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6943789Z out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T21:02:11.6943858Z 2025-03-04T21:02:11.6944148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6944624Z x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6944702Z 2025-03-04T21:02:11.6944987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6946618Z x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6946702Z 2025-03-04T21:02:11.6947007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6947164Z out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T21:02:11.6947251Z 2025-03-04T21:02:11.6947532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6947993Z x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6948072Z 2025-03-04T21:02:11.6948360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6949921Z x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6950000Z 2025-03-04T21:02:11.6950282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6950446Z x_189 += out_115; out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T21:02:11.6950513Z 2025-03-04T21:02:11.6950822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6950987Z out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T21:02:11.6951060Z 2025-03-04T21:02:11.6951314Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6951743Z x_190: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6951816Z 2025-03-04T21:02:11.6952082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6953641Z x_191: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_190 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6953712Z 2025-03-04T21:02:11.6954004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6954166Z out_120: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_191); x_191 = None 2025-03-04T21:02:11.6954234Z 2025-03-04T21:02:11.6954492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6954918Z x_192: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_120 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6954992Z 2025-03-04T21:02:11.6955259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6956796Z x_193: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_192 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6956871Z 2025-03-04T21:02:11.6957157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6957302Z out_121: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_193); x_193 = None 2025-03-04T21:02:11.6957386Z 2025-03-04T21:02:11.6957660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6958094Z x_194: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_121 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6958168Z 2025-03-04T21:02:11.6958435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6959963Z x_195: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_194 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6960042Z 2025-03-04T21:02:11.6960299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6960766Z x_196: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_119 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:02:11.6960851Z 2025-03-04T21:02:11.6961134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6962727Z x_197: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_196 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6962806Z 2025-03-04T21:02:11.6963097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6963250Z x_195 += x_197; out_122: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_195; x_195 = x_197 = None 2025-03-04T21:02:11.6963323Z 2025-03-04T21:02:11.6963611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6963766Z out_123: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_122); out_122 = None 2025-03-04T21:02:11.6963831Z 2025-03-04T21:02:11.6964110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6964549Z x_198: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6964619Z 2025-03-04T21:02:11.6964886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6966433Z x_199: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_198 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6966510Z 2025-03-04T21:02:11.6966804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6966950Z out_124: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_199); x_199 = None 2025-03-04T21:02:11.6967016Z 2025-03-04T21:02:11.6967294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6967723Z x_200: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_124 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6967798Z 2025-03-04T21:02:11.6968063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6969596Z x_201: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_200 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6969670Z 2025-03-04T21:02:11.6969953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6970098Z out_125: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_201); x_201 = None 2025-03-04T21:02:11.6970162Z 2025-03-04T21:02:11.6970420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6970875Z x_202: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_125 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6970966Z 2025-03-04T21:02:11.6971236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6972777Z x_203: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_202 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6972854Z 2025-03-04T21:02:11.6973137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6973305Z x_203 += out_123; out_126: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_203; x_203 = out_123 = None 2025-03-04T21:02:11.6973371Z 2025-03-04T21:02:11.6973667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6973834Z out_127: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_126); out_126 = None 2025-03-04T21:02:11.6973907Z 2025-03-04T21:02:11.6974176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6974745Z x_204: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:02:11.6974825Z 2025-03-04T21:02:11.6975141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6976814Z x_205: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_204 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6976892Z 2025-03-04T21:02:11.6977224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6977378Z out_128: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_205); x_205 = None 2025-03-04T21:02:11.6977464Z 2025-03-04T21:02:11.6977774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6978318Z x_206: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_128 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:02:11.6978392Z 2025-03-04T21:02:11.6978713Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6980415Z x_207: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_206 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6980494Z 2025-03-04T21:02:11.6980821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6980976Z out_129: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_207); x_207 = None 2025-03-04T21:02:11.6981082Z 2025-03-04T21:02:11.6981375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6981880Z x_208: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_129 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:02:11.6981956Z 2025-03-04T21:02:11.6982253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:02:11.6983822Z x_209: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_208 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:02:11.6983889Z 2025-03-04T21:02:11.6984175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:02:11.6984333Z x_209 += out_127; out_130: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_209; x_209 = out_127 = None 2025-03-04T21:02:11.6984408Z 2025-03-04T21:02:11.6984714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:02:11.6984874Z out_131: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_130); out_130 = None 2025-03-04T21:02:11.6984954Z 2025-03-04T21:02:11.6985404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:02:11.6985559Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:02:11.6985633Z 2025-03-04T21:02:11.6985936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:11.6986082Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:02:11.6986150Z 2025-03-04T21:02:11.6986594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:02:11.6986769Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:02:11.6986836Z 2025-03-04T21:02:11.6987132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:11.6987272Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:02:11.6987345Z 2025-03-04T21:02:11.6987718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:02:11.6987927Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:02:11.6988028Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:02:11.6988273Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:02:11.6988345Z 2025-03-04T21:02:11.6988693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:02:11.6988822Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:02:11.6988898Z 2025-03-04T21:02:11.6989227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:02:11.6989359Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:02:11.6989427Z 2025-03-04T21:02:11.6989819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:02:11.6990035Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:02:11.6990109Z 2025-03-04T21:02:11.6990532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:02:11.6990667Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:02:11.6991097Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:02:11.6991267Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:02:11.6991406Z x_210: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:02:11.6991480Z 2025-03-04T21:02:11.6991782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:02:11.6991917Z tensor: "f32[82125, 4][4, 1]cpu" = x_210.to(torch.float32); x_210 = None 2025-03-04T21:02:11.6991981Z 2025-03-04T21:02:11.6992243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:11.6993026Z x_211: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_131, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_131 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:02:11.6993097Z 2025-03-04T21:02:11.6993401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:02:11.6993597Z x_212: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_211, inplace = False); x_211 = None 2025-03-04T21:02:11.6993672Z 2025-03-04T21:02:11.6994055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:02:11.6994924Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:02:11.6995018Z 2025-03-04T21:02:11.6995388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:02:11.6996220Z x_213: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_212 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:02:11.6996290Z 2025-03-04T21:02:11.6996643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:02:11.6996799Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:02:11.6996948Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:02:11.6997015Z 2025-03-04T21:02:11.6997435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:02:11.6997596Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_213.view(4, -1, 4, 73, 75); x_213 = None 2025-03-04T21:02:11.6997796Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:02:11.6997998Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:02:11.6998076Z 2025-03-04T21:02:11.6998483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:02:11.6998698Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:02:11.6998765Z 2025-03-04T21:02:11.6999212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:02:11.6999365Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:02:11.6999522Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:02:11.6999677Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:02:11.6999753Z 2025-03-04T21:02:11.7000130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:02:11.7000315Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:02:11.7000382Z 2025-03-04T21:02:11.7000703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:02:11.7000863Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:02:11.7000938Z 2025-03-04T21:02:11.7001254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:02:11.7001395Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:02:11.7001529Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:11.7001675Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:02:11.7001748Z 2025-03-04T21:02:11.7002067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:02:11.7002197Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:02:11.7002317Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:02:11.7002469Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:02:11.7002535Z 2025-03-04T21:02:11.7002853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:02:11.7002975Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:11.7003071Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:02:11.7003194Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:02:11.7003266Z 2025-03-04T21:02:11.7003576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:02:11.7003729Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:02:11.7003836Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:02:11.7003988Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:02:11.7004056Z 2025-03-04T21:02:11.7004399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:02:11.7004553Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:02:11.7004675Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:02:11.7004740Z 2025-03-04T21:02:11.7005050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:02:11.7005202Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:02:11.7005322Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:02:11.7005388Z 2025-03-04T21:02:11.7005717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:02:11.7005874Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:02:11.7005994Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:02:11.7006063Z 2025-03-04T21:02:11.7006380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:02:11.7006565Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:02:11.7006704Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:02:11.7006770Z 2025-03-04T21:02:11.7007126Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:02:11.7007271Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:02:11.7007345Z 2025-03-04T21:02:11.7007684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:02:11.7007831Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:02:11.7007895Z 2025-03-04T21:02:11.7008255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:02:11.7008406Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:02:11.7008533Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:02:11.7008694Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:02:11.7008833Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:02:11.7008906Z 2025-03-04T21:02:11.7009252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:02:11.7009397Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:02:11.7009521Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:02:11.7009694Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:02:11.7009830Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:02:11.7009917Z 2025-03-04T21:02:11.7010248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:02:11.7010376Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:02:11.7010535Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:02:11.7010675Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:02:11.7010740Z 2025-03-04T21:02:11.7011079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:02:11.7011200Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:02:11.7011374Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:02:11.7011526Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:02:11.7011599Z 2025-03-04T21:02:11.7011917Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:02:11.7012023Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:02:11.7012142Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:02:11.7012215Z 2025-03-04T21:02:11.7012528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:02:11.7012651Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:02:11.7012768Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:02:11.7012842Z 2025-03-04T21:02:11.7013146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:02:11.7013270Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:02:11.7013395Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:02:11.7013468Z 2025-03-04T21:02:11.7013769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:02:11.7013893Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:02:11.7014021Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:02:11.7014099Z 2025-03-04T21:02:11.7014522Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:02:11.7014726Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:02:11.7014794Z 2025-03-04T21:02:11.7015147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:02:11.7015313Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:02:11.7015388Z 2025-03-04T21:02:11.7015794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:02:11.7016026Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:02:11.7016115Z 2025-03-04T21:02:11.7016609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:02:11.7016772Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:02:11.7016838Z 2025-03-04T21:02:11.7017143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:11.7017283Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:02:11.7017358Z 2025-03-04T21:02:11.7017794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:02:11.7017920Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:02:11.7018042Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:02:11.7018170Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:02:11.7018237Z 2025-03-04T21:02:11.7018706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:02:11.7018871Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:02:11.7019135Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:02:11.7019201Z 2025-03-04T21:02:11.7019662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:02:11.7019830Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:02:11.7019903Z 2025-03-04T21:02:11.7020196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:11.7020355Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:02:11.7020419Z 2025-03-04T21:02:11.7020809Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:02:11.7020959Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:02:11.7021032Z 2025-03-04T21:02:11.7021328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:02:11.7021478Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:02:11.7021544Z 2025-03-04T21:02:11.7021923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:02:11.7022062Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:02:11.7022137Z 2025-03-04T21:02:11.7022629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:02:11.7022792Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:02:11.7022912Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:02:11.7023074Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:02:11.7023205Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:02:11.7023278Z 2025-03-04T21:02:11.7023645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:02:11.7023770Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:02:11.7023842Z 2025-03-04T21:02:30.4573914Z 2025-03-04T21:02:30.4574681Z class GraphModule(torch.nn.Module): 2025-03-04T21:02:30.4576768Z def forward(self, L_features_res5_: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:02:30.4578394Z l_features_res5_ = L_features_res5_ 2025-03-04T21:02:30.4584835Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:02:30.4586582Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:02:30.4587257Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:02:30.4593234Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:02:30.4594994Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:02:30.4595730Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:02:30.4601672Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:02:30.4602298Z 2025-03-04T21:02:30.4603064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:02:30.4603874Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:02:30.4604177Z 2025-03-04T21:02:30.4604610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:30.4605185Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:02:30.4605456Z 2025-03-04T21:02:30.4606259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:02:30.4606982Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:02:30.4607264Z 2025-03-04T21:02:30.4607660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:30.4608155Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:02:30.4608426Z 2025-03-04T21:02:30.4608898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:02:30.4609519Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:02:30.4609864Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:02:30.4610146Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:02:30.4610390Z 2025-03-04T21:02:30.4610875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:02:30.4611399Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:02:30.4611651Z 2025-03-04T21:02:30.4612071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:02:30.4612572Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:02:30.4612819Z 2025-03-04T21:02:30.4613299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:02:30.4614002Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:02:30.4614339Z 2025-03-04T21:02:30.4614999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:02:30.4615646Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:02:30.4616183Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:02:30.4616703Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:02:30.4617000Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:02:30.4617231Z 2025-03-04T21:02:30.4617630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:02:30.4618117Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:02:30.4618368Z 2025-03-04T21:02:30.4618722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:30.4619682Z x_1: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(l_features_res5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res5_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:02:30.4620437Z 2025-03-04T21:02:30.4620847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:02:30.4621401Z x_2: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:02:30.4621713Z 2025-03-04T21:02:30.4622192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:02:30.4623277Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:02:30.4624032Z 2025-03-04T21:02:30.4624489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:02:30.4625532Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:02:30.4626255Z 2025-03-04T21:02:30.4626685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:02:30.4627238Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:02:30.4627611Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:02:30.4627874Z 2025-03-04T21:02:30.4628385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:02:30.4629002Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T21:02:30.4629383Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:02:30.4629777Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:02:30.4630072Z 2025-03-04T21:02:30.4630602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:02:30.4631270Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:02:30.4631592Z 2025-03-04T21:02:30.4632118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:02:30.4632773Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:02:30.4633132Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:02:30.4633476Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:02:30.4633747Z 2025-03-04T21:02:30.4634248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:02:30.4634881Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:02:30.4635182Z 2025-03-04T21:02:30.4635603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:02:30.4636133Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:02:30.4636414Z 2025-03-04T21:02:30.4636836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:02:30.4637362Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:02:30.4637686Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:30.4638028Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:02:30.4638307Z 2025-03-04T21:02:30.4638749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:02:30.4639271Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:02:30.4639584Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:02:30.4639921Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:02:30.4640199Z 2025-03-04T21:02:30.4640612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:02:30.4641147Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:30.4641434Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:02:30.4641725Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:02:30.4641986Z 2025-03-04T21:02:30.4642418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:02:30.4642969Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:02:30.4643284Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:02:30.4643576Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:02:30.4643846Z 2025-03-04T21:02:30.4644285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:02:30.4644818Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:02:30.4645159Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:02:30.4645406Z 2025-03-04T21:02:30.4645813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:02:30.4646344Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:02:30.4646686Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:02:30.4646928Z 2025-03-04T21:02:30.4647332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:02:30.4647867Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:02:30.4648237Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:02:30.4648482Z 2025-03-04T21:02:30.4648908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:02:30.4649467Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:02:30.4649830Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:02:30.4650075Z 2025-03-04T21:02:30.4650535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:02:30.4651093Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:02:30.4651366Z 2025-03-04T21:02:30.4651803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:02:30.4652350Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:02:30.4652619Z 2025-03-04T21:02:30.4653121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:02:30.4653685Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:02:30.4654028Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:02:30.4654390Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:02:30.4654764Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:02:30.4655160Z 2025-03-04T21:02:30.4655634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:02:30.4656219Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:02:30.4656550Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:02:30.4656892Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:02:30.4657255Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:02:30.4657525Z 2025-03-04T21:02:30.4657963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:02:30.4658491Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:02:30.4658838Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:02:30.4659197Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:02:30.4659461Z 2025-03-04T21:02:30.4659902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:02:30.4660412Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:02:30.4660745Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:02:30.4661097Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:02:30.4661356Z 2025-03-04T21:02:30.4661787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:02:30.4662260Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:02:30.4662552Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:02:30.4662795Z 2025-03-04T21:02:30.4663196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:02:30.4663665Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:02:30.4663934Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:02:30.4664172Z 2025-03-04T21:02:30.4664570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:02:30.4665051Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:02:30.4665350Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:02:30.4665604Z 2025-03-04T21:02:30.4666023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:02:30.4666515Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:02:30.4666814Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:02:30.4667058Z 2025-03-04T21:02:30.4667501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:02:30.4668088Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:02:30.4668402Z 2025-03-04T21:02:30.4668828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:02:30.4669387Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:02:30.4669677Z 2025-03-04T21:02:30.4670153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:02:30.4670769Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:02:30.4671064Z 2025-03-04T21:02:30.4671637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:02:30.4672322Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:02:30.4672580Z 2025-03-04T21:02:30.4672971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:30.4673472Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:02:30.4673741Z 2025-03-04T21:02:30.4674272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:02:30.4674879Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:02:30.4675153Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:02:30.4675427Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:02:30.4675658Z 2025-03-04T21:02:30.4676236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:02:30.4676936Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:02:30.4677390Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:02:30.4677746Z 2025-03-04T21:02:30.4678296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:02:30.4678982Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:02:30.4679259Z 2025-03-04T21:02:30.4679633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:30.4680144Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:02:30.4680414Z 2025-03-04T21:02:30.4680873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:02:30.4681442Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:02:30.4681704Z 2025-03-04T21:02:30.4682084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:02:30.4682590Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:02:30.4682855Z 2025-03-04T21:02:30.4683315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:02:30.4683884Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:02:30.4684149Z 2025-03-04T21:02:30.4684717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:02:30.4685393Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:02:30.4685711Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:02:30.4686046Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:02:30.4686393Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:02:30.4686651Z 2025-03-04T21:02:30.4687109Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:02:30.4687651Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:02:30.4687893Z 2025-03-04T21:02:30.4688042Z 2025-03-04T21:02:30.4688430Z class GraphModule(torch.nn.Module): 2025-03-04T21:02:30.4689859Z def forward(self, L_features_res5_: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:02:30.4691332Z l_features_res5_ = L_features_res5_ 2025-03-04T21:02:30.4691734Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:02:30.4692272Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:02:30.4692754Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:02:30.4693296Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:02:30.4693883Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:02:30.4694481Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:02:30.4695089Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:02:30.4695468Z 2025-03-04T21:02:30.4696046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:02:30.4696735Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:02:30.4697020Z 2025-03-04T21:02:30.4697432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:30.4697931Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:02:30.4698193Z 2025-03-04T21:02:30.4698722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:02:30.4699359Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:02:30.4699632Z 2025-03-04T21:02:30.4700020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:30.4700514Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:02:30.4700774Z 2025-03-04T21:02:30.4701245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:02:30.4701857Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:02:30.4702195Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:02:30.4702466Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:02:30.4702704Z 2025-03-04T21:02:30.4703117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:02:30.4703643Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:02:30.4703886Z 2025-03-04T21:02:30.4704327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:02:30.4704875Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:02:30.4705122Z 2025-03-04T21:02:30.4705606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:02:30.4706262Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:02:30.4706598Z 2025-03-04T21:02:30.4707113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:02:30.4707716Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:02:30.4708243Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:02:30.4708748Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:02:30.4709043Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:02:30.4709276Z 2025-03-04T21:02:30.4709671Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:02:30.4710150Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:02:30.4710399Z 2025-03-04T21:02:30.4710771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:02:30.4711714Z x_1: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(l_features_res5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res5_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:02:30.4712427Z 2025-03-04T21:02:30.4712789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:02:30.4713303Z x_2: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:02:30.4713612Z 2025-03-04T21:02:30.4714086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:02:30.4715181Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:02:30.4715941Z 2025-03-04T21:02:30.4716402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:02:30.4718461Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:02:30.4719207Z 2025-03-04T21:02:30.4719647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:02:30.4720203Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:02:30.4720555Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:02:30.4720825Z 2025-03-04T21:02:30.4721334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:02:30.4721958Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T21:02:30.4722347Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:02:30.4722776Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:02:30.4723078Z 2025-03-04T21:02:30.4723572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:02:30.4724248Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:02:30.4724578Z 2025-03-04T21:02:30.4725099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:02:30.4725762Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:02:30.4726121Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:02:30.4726459Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:02:30.4726725Z 2025-03-04T21:02:30.4727189Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:02:30.4727784Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:02:30.4728080Z 2025-03-04T21:02:30.4728496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:02:30.4729011Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:02:30.4729276Z 2025-03-04T21:02:30.4729681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:02:30.4730183Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:02:30.4730497Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:30.4730825Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:02:30.4731095Z 2025-03-04T21:02:30.4731505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:02:30.4732006Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:02:30.4732335Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:02:30.4732661Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:02:30.4732961Z 2025-03-04T21:02:30.4733375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:02:30.4733880Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:30.4734161Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:02:30.4734436Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:02:30.4734696Z 2025-03-04T21:02:30.4735192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:02:30.4735731Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:02:30.4736049Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:02:30.4736326Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:02:30.4736579Z 2025-03-04T21:02:30.4737020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:02:30.4737538Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:02:30.4737871Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:02:30.4738112Z 2025-03-04T21:02:30.4738509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:02:30.4739046Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:02:30.4739372Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:02:30.4739611Z 2025-03-04T21:02:30.4740003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:02:30.4740515Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:02:30.4740844Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:02:30.4741083Z 2025-03-04T21:02:30.4741477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:02:30.4742022Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:02:30.4742381Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:02:30.4742618Z 2025-03-04T21:02:30.4743039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:02:30.4743591Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:02:30.4743850Z 2025-03-04T21:02:30.4744276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:02:30.4744809Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:02:30.4745073Z 2025-03-04T21:02:30.4745505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:02:30.4746075Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:02:30.4746419Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:02:30.4746761Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:02:30.4747125Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:02:30.4747385Z 2025-03-04T21:02:30.4747823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:02:30.4748366Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:02:30.4748689Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:02:30.4749028Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:02:30.4749378Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:02:30.4749649Z 2025-03-04T21:02:30.4750087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:02:30.4750588Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:02:30.4750915Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:02:30.4751260Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:02:30.4751511Z 2025-03-04T21:02:30.4751925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:02:30.4752558Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:02:30.4752892Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:02:30.4753243Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:02:30.4753495Z 2025-03-04T21:02:30.4753890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:02:30.4754346Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:02:30.4754614Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:02:30.4754854Z 2025-03-04T21:02:30.4755245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:02:30.4755701Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:02:30.4755962Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:02:30.4756199Z 2025-03-04T21:02:30.4756584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:02:30.4757048Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:02:30.4757340Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:02:30.4757586Z 2025-03-04T21:02:30.4757970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:02:30.4758432Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:02:30.4758723Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:02:30.4759001Z 2025-03-04T21:02:30.4759454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:02:30.4760029Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:02:30.4760318Z 2025-03-04T21:02:30.4760736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:02:30.4761275Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:02:30.4761556Z 2025-03-04T21:02:30.4762019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:02:30.4762621Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:02:30.4762908Z 2025-03-04T21:02:30.4763492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:02:30.4764154Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:02:30.4764410Z 2025-03-04T21:02:30.4764786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:30.4765275Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:02:30.4765557Z 2025-03-04T21:02:30.4766074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:02:30.4766669Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:02:30.4766937Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:02:30.4767204Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:02:30.4767432Z 2025-03-04T21:02:30.4767980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:02:30.4768660Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:02:30.4769120Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:02:30.4769478Z 2025-03-04T21:02:30.4770025Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:02:30.4770708Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:02:30.4770994Z 2025-03-04T21:02:30.4771378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:30.4771888Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:02:30.4772163Z 2025-03-04T21:02:30.4772666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:02:30.4773271Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:02:30.4773540Z 2025-03-04T21:02:30.4773988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:02:30.4774560Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:02:30.4774837Z 2025-03-04T21:02:30.4775415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:02:30.4776028Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:02:30.4776304Z 2025-03-04T21:02:30.4777022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:02:30.4777754Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:02:30.4778091Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:02:30.4778440Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:02:30.4778800Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:02:30.4779069Z 2025-03-04T21:02:30.4779572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:02:30.4780233Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:02:30.4780524Z 2025-03-04T21:02:33.5468574Z 2025-03-04T21:02:33.5471813Z class GraphModule(torch.nn.Module): 2025-03-04T21:02:33.5472474Z def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"): 2025-03-04T21:02:33.5472945Z l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_ 2025-03-04T21:02:33.5473214Z l_anchors_0_tensor = L_anchors_0_tensor 2025-03-04T21:02:33.5473497Z l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_ 2025-03-04T21:02:33.5473745Z 2025-03-04T21:02:33.5474310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:02:33.5475025Z pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4); l_pred_anchor_deltas_0_ = None 2025-03-04T21:02:33.5475387Z 2025-03-04T21:02:33.5475948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:02:33.5476649Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0); l_anchors_0_tensor = None 2025-03-04T21:02:33.5477055Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:02:33.5477409Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:02:33.5477681Z 2025-03-04T21:02:33.5478168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:02:33.5479035Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float(); pred_anchor_deltas_i = None 2025-03-04T21:02:33.5479391Z 2025-03-04T21:02:33.5479804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:02:33.5480325Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:02:33.5480591Z 2025-03-04T21:02:33.5481000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:02:33.5481507Z getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:02:33.5481822Z getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:33.5482174Z widths: "f32[328500][1]cpu" = getitem - getitem_1; getitem = getitem_1 = None 2025-03-04T21:02:33.5482443Z 2025-03-04T21:02:33.5482856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:02:33.5483428Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:02:33.5483741Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:02:33.5484087Z heights: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:02:33.5484365Z 2025-03-04T21:02:33.5484775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:02:33.5485272Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:33.5485543Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:02:33.5485861Z ctr_x: "f32[328500][1]cpu" = getitem_4 + mul; getitem_4 = mul = None 2025-03-04T21:02:33.5486112Z 2025-03-04T21:02:33.5486521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:02:33.5487042Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:02:33.5487335Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:02:33.5487610Z ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1; getitem_5 = mul_1 = None 2025-03-04T21:02:33.5487861Z 2025-03-04T21:02:33.5488480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:02:33.5489006Z getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:02:33.5489342Z dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0; getitem_6 = None 2025-03-04T21:02:33.5489583Z 2025-03-04T21:02:33.5489977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:02:33.5490487Z getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:02:33.5490813Z dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0; getitem_7 = None 2025-03-04T21:02:33.5491043Z 2025-03-04T21:02:33.5491435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:02:33.5491934Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:02:33.5492258Z dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:02:33.5492495Z 2025-03-04T21:02:33.5492923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:02:33.5493490Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:02:33.5493845Z dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:02:33.5494078Z 2025-03-04T21:02:33.5494511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:02:33.5495182Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:02:33.5495469Z 2025-03-04T21:02:33.5495940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:02:33.5496494Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:02:33.5496763Z 2025-03-04T21:02:33.5497253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:02:33.5497817Z getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:02:33.5498155Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10; dx = getitem_10 = None 2025-03-04T21:02:33.5498505Z getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:02:33.5498871Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11; mul_2 = getitem_11 = None 2025-03-04T21:02:33.5499146Z 2025-03-04T21:02:33.5499601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:02:33.5500192Z getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:02:33.5500524Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12; dy = getitem_12 = None 2025-03-04T21:02:33.5500865Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:02:33.5501223Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13; mul_3 = getitem_13 = None 2025-03-04T21:02:33.5501489Z 2025-03-04T21:02:33.5501925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:02:33.5502448Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:02:33.5502794Z getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:02:33.5503155Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14; exp = getitem_14 = None 2025-03-04T21:02:33.5503418Z 2025-03-04T21:02:33.5503857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:02:33.5504373Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:02:33.5504720Z getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:02:33.5505084Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15; exp_1 = getitem_15 = None 2025-03-04T21:02:33.5505347Z 2025-03-04T21:02:33.5505761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:02:33.5506240Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:02:33.5506538Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:02:33.5506801Z 2025-03-04T21:02:33.5507211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:02:33.5507682Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:02:33.5507942Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:02:33.5508176Z 2025-03-04T21:02:33.5508552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:02:33.5509012Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:02:33.5509303Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:02:33.5509551Z 2025-03-04T21:02:33.5509932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:02:33.5510396Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:02:33.5510809Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:02:33.5511059Z 2025-03-04T21:02:33.5511499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:02:33.5512077Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:02:33.5512373Z 2025-03-04T21:02:33.5512793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:02:33.5513363Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:02:33.5513653Z 2025-03-04T21:02:33.5514122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:02:33.5514722Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:02:33.5515011Z 2025-03-04T21:02:33.5515572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:02:33.5516254Z arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:02:33.5516505Z 2025-03-04T21:02:33.5516883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:33.5517363Z batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:02:33.5517617Z 2025-03-04T21:02:33.5518133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:02:33.5518787Z topk = l_pred_objectness_logits_0_.topk(6000, dim = 1); l_pred_objectness_logits_0_ = None 2025-03-04T21:02:33.5519120Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:02:33.5519392Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:02:33.5519624Z 2025-03-04T21:02:33.5520188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:02:33.5520877Z getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:02:33.5521325Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)]; proposals_i_1 = getitem_18 = topk_idx = None 2025-03-04T21:02:33.5521678Z 2025-03-04T21:02:33.5522214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:02:33.5522877Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:02:33.5523167Z 2025-03-04T21:02:33.5523553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:02:33.5524057Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:02:33.5524331Z 2025-03-04T21:02:33.5524830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:02:33.5525406Z getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:02:33.5525668Z 2025-03-04T21:02:33.5526072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:02:33.5526559Z tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32); getitem_20 = None 2025-03-04T21:02:33.5526843Z 2025-03-04T21:02:33.5527297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:02:33.5527860Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:02:33.5528119Z 2025-03-04T21:02:33.5528692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:02:33.5529374Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor); tensor = None 2025-03-04T21:02:33.5529690Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:02:33.5530028Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:02:33.5530384Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:02:33.5530646Z 2025-03-04T21:02:33.5531114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:02:33.5531658Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:02:33.5531902Z 2025-03-04T21:02:40.1363318Z 2025-03-04T21:02:40.1363955Z class GraphModule(torch.nn.Module): 2025-03-04T21:02:40.1366539Z def forward(self, L_stack0_: "f32[3225, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"): 2025-03-04T21:02:40.1368846Z l_stack0_ = L_stack0_ 2025-03-04T21:02:40.1371329Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:02:40.1371981Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:02:40.1372526Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:02:40.1373021Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:02:40.1373829Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:02:40.1374415Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:02:40.1375057Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:02:40.1375628Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:02:40.1376187Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:02:40.1376596Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:02:40.1376999Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:02:40.1377395Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:02:40.1377695Z 2025-03-04T21:02:40.1378116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:02:40.1378621Z x: "f32[3225, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:02:40.1379341Z x_1: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:02:40.1380168Z x_2: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:02:40.1380910Z x_3: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:02:40.1381630Z x_4: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:02:40.1381918Z 2025-03-04T21:02:40.1382334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:02:40.1383369Z scores: "f32[3225, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:02:40.1384113Z 2025-03-04T21:02:40.1384539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:02:40.1385554Z proposal_deltas: "f32[3225, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:02:40.1386298Z 2025-03-04T21:02:40.1386683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:02:40.1387162Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:02:40.1387428Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:02:40.1387670Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:02:40.1387978Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:02:40.1388639Z getitem_2: "Sym(1225 - s0)" = size_1[0] 2025-03-04T21:02:40.1388902Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:02:40.1389135Z 2025-03-04T21:02:40.1389526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:02:40.1390488Z proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:02:40.1391266Z 2025-03-04T21:02:40.1391732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:02:40.1392314Z deltas: "f32[3225, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:02:40.1392594Z 2025-03-04T21:02:40.1393001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:02:40.1393537Z boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:02:40.1393822Z 2025-03-04T21:02:40.1394217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:02:40.1394719Z getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:02:40.1395024Z getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:40.1395345Z widths: "f32[3225][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:02:40.1395605Z 2025-03-04T21:02:40.1396010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:02:40.1396496Z getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:02:40.1396787Z getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:02:40.1397101Z heights: "f32[3225][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:02:40.1397373Z 2025-03-04T21:02:40.1397813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:02:40.1398335Z getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:40.1398608Z mul: "f32[3225][1]cpu" = 0.5 * widths 2025-03-04T21:02:40.1398868Z ctr_x: "f32[3225][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:02:40.1399106Z 2025-03-04T21:02:40.1399502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:02:40.1400001Z getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:02:40.1400289Z mul_1: "f32[3225][1]cpu" = 0.5 * heights 2025-03-04T21:02:40.1400555Z ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:02:40.1400798Z 2025-03-04T21:02:40.1401221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:02:40.1401736Z getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:02:40.1402103Z dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:02:40.1402342Z 2025-03-04T21:02:40.1402727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:02:40.1403220Z getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:02:40.1403540Z dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:02:40.1403776Z 2025-03-04T21:02:40.1404187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:02:40.1404686Z getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:02:40.1405013Z dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:02:40.1405249Z 2025-03-04T21:02:40.1405633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:02:40.1406162Z getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:02:40.1406505Z dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:02:40.1406728Z 2025-03-04T21:02:40.1407153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:02:40.1407681Z dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:02:40.1407943Z 2025-03-04T21:02:40.1408360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:02:40.1408875Z dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:02:40.1409129Z 2025-03-04T21:02:40.1409561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:02:40.1410097Z getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:02:40.1410423Z mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:02:40.1410797Z getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:02:40.1412259Z pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:02:40.1413289Z 2025-03-04T21:02:40.1414952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:02:40.1415865Z getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:02:40.1416195Z mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:02:40.1416529Z getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:02:40.1416877Z pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:02:40.1417144Z 2025-03-04T21:02:40.1417578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:02:40.1418306Z exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:02:40.1418755Z getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:02:40.1419120Z pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:02:40.1419379Z 2025-03-04T21:02:40.1419811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:02:40.1420324Z exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:02:40.1420658Z getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:02:40.1421078Z pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:02:40.1421340Z 2025-03-04T21:02:40.1421762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:02:40.1422246Z mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:02:40.1422520Z x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:02:40.1422762Z 2025-03-04T21:02:40.1423170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:02:40.1423641Z mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:02:40.1423906Z y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:02:40.1424149Z 2025-03-04T21:02:40.1424543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:02:40.1425029Z mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:02:40.1425323Z x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:02:40.1425571Z 2025-03-04T21:02:40.1425971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:02:40.1426443Z mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:02:40.1426719Z y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:02:40.1426966Z 2025-03-04T21:02:40.1427400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:02:40.1428036Z pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:02:40.1428351Z 2025-03-04T21:02:40.1428780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:02:40.1429334Z predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320)); pred_boxes = None 2025-03-04T21:02:40.1429625Z 2025-03-04T21:02:40.1430069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:02:40.1430676Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:02:40.1431041Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:02:40.1431331Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:02:40.1431636Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:02:40.1431971Z getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:02:40.1432229Z 2025-03-04T21:02:40.1432597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:02:40.1433145Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:02:40.1433487Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:02:40.1433728Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:02:40.1434089Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:02:40.1434466Z getitem_26: "Sym(1225 - s0)" = size_3[0] 2025-03-04T21:02:40.1434708Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:02:40.1434931Z 2025-03-04T21:02:40.1435351Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:02:40.1435902Z probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:02:40.1436186Z 2025-03-04T21:02:40.1436623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:02:40.1437220Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:02:40.1437575Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:02:40.1437869Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:02:40.1438179Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:02:40.1438496Z getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:02:40.1438756Z 2025-03-04T21:02:40.1439328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:02:40.1440015Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:02:40.1440356Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:02:40.1440700Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:02:40.1441073Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:02:40.1441399Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:02:40.1441646Z 2025-03-04T21:02:40.1442092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:02:40.1442642Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:02:40.1442886Z 2025-03-04T21:02:40.1443028Z 2025-03-04T21:02:40.1443127Z class GraphModule(torch.nn.Module): 2025-03-04T21:02:40.1445070Z def forward(self, L_stack0_: "f32[3225, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"): 2025-03-04T21:02:40.1447121Z l_stack0_ = L_stack0_ 2025-03-04T21:02:40.1447482Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:02:40.1448009Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:02:40.1448508Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:02:40.1448995Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:02:40.1449536Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:02:40.1450122Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:02:40.1450706Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:02:40.1451281Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:02:40.1451780Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:02:40.1452206Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:02:40.1452620Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:02:40.1453029Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:02:40.1453340Z 2025-03-04T21:02:40.1453735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:02:40.1454226Z x: "f32[3225, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:02:40.1455155Z x_1: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:02:40.1455950Z x_2: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:02:40.1456712Z x_3: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:02:40.1457452Z x_4: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:02:40.1457756Z 2025-03-04T21:02:40.1458164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:02:40.1459166Z scores: "f32[3225, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:02:40.1459897Z 2025-03-04T21:02:40.1460318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:02:40.1461332Z proposal_deltas: "f32[3225, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:02:40.1462092Z 2025-03-04T21:02:40.1462481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:02:40.1462963Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:02:40.1463227Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:02:40.1463465Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:02:40.1463749Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:02:40.1464015Z getitem_2: "Sym(1225 - s0)" = size_1[0] 2025-03-04T21:02:40.1464277Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:02:40.1464498Z 2025-03-04T21:02:40.1464873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:02:40.1465829Z proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:02:40.1468178Z 2025-03-04T21:02:40.1468666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:02:40.1469260Z deltas: "f32[3225, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:02:40.1469544Z 2025-03-04T21:02:40.1469949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:02:40.1470481Z boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:02:40.1470765Z 2025-03-04T21:02:40.1471214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:02:40.1472504Z getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:02:40.1472828Z getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:40.1473154Z widths: "f32[3225][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:02:40.1473424Z 2025-03-04T21:02:40.1473854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:02:40.1474364Z getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:02:40.1474669Z getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:02:40.1475005Z heights: "f32[3225][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:02:40.1475284Z 2025-03-04T21:02:40.1475697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:02:40.1476927Z getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:40.1477212Z mul: "f32[3225][1]cpu" = 0.5 * widths 2025-03-04T21:02:40.1477479Z ctr_x: "f32[3225][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:02:40.1477723Z 2025-03-04T21:02:40.1478132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:02:40.1478657Z getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:02:40.1478994Z mul_1: "f32[3225][1]cpu" = 0.5 * heights 2025-03-04T21:02:40.1479267Z ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:02:40.1479515Z 2025-03-04T21:02:40.1479937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:02:40.1480458Z getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:02:40.1480793Z dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:02:40.1481025Z 2025-03-04T21:02:40.1481421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:02:40.1481921Z getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:02:40.1482242Z dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:02:40.1482474Z 2025-03-04T21:02:40.1482857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:02:40.1483355Z getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:02:40.1483669Z dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:02:40.1483898Z 2025-03-04T21:02:40.1484283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:02:40.1484813Z getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:02:40.1485150Z dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:02:40.1485385Z 2025-03-04T21:02:40.1485831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:02:40.1486380Z dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:02:40.1486639Z 2025-03-04T21:02:40.1487053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:02:40.1487570Z dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:02:40.1487821Z 2025-03-04T21:02:40.1488372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:02:40.1488905Z getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:02:40.1489218Z mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:02:40.1489550Z getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:02:40.1489973Z pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:02:40.1490230Z 2025-03-04T21:02:40.1490657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:02:40.1491184Z getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:02:40.1491490Z mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:02:40.1491807Z getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:02:40.1492177Z pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:02:40.1492437Z 2025-03-04T21:02:40.1492864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:02:40.1493369Z exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:02:40.1493698Z getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:02:40.1494042Z pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:02:40.1494289Z 2025-03-04T21:02:40.1494709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:02:40.1495302Z exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:02:40.1495656Z getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:02:40.1496029Z pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:02:40.1496284Z 2025-03-04T21:02:40.1496691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:02:40.1497160Z mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:02:40.1497430Z x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:02:40.1497673Z 2025-03-04T21:02:40.1498075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:02:40.1498539Z mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:02:40.1498804Z y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:02:40.1499065Z 2025-03-04T21:02:40.1499469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:02:40.1499979Z mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:02:40.1500281Z x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:02:40.1500534Z 2025-03-04T21:02:40.1500932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:02:40.1501422Z mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:02:40.1501719Z y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:02:40.1501971Z 2025-03-04T21:02:40.1502419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:02:40.1503040Z pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:02:40.1503335Z 2025-03-04T21:02:40.1503770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:02:40.1504334Z predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320)); pred_boxes = None 2025-03-04T21:02:40.1504624Z 2025-03-04T21:02:40.1505077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:02:40.1505732Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:02:40.1506098Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:02:40.1506393Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:02:40.1506701Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:02:40.1507018Z getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:02:40.1507278Z 2025-03-04T21:02:40.1507661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:02:40.1508223Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:02:40.1508575Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:02:40.1508827Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:02:40.1509193Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:02:40.1509566Z getitem_26: "Sym(1225 - s0)" = size_3[0] 2025-03-04T21:02:40.1509811Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:02:40.1510033Z 2025-03-04T21:02:40.1510444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:02:40.1510990Z probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:02:40.1511277Z 2025-03-04T21:02:40.1511705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:02:40.1512287Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:02:40.1512660Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:02:40.1512969Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:02:40.1513270Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:02:40.1513591Z getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:02:40.1513848Z 2025-03-04T21:02:40.1514385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:02:40.1515082Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:02:40.1515424Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:02:40.1515760Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:02:40.1516094Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:02:40.1516409Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:02:40.1516648Z 2025-03-04T21:02:40.1517086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:02:40.1517597Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:02:40.1517833Z 2025-03-04T21:02:40.1517964Z 2025-03-04T21:02:40.1518061Z class GraphModule(torch.nn.Module): 2025-03-04T21:02:40.1520631Z def forward(self, L_stack0_: "f32[3225, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"): 2025-03-04T21:02:40.1523588Z l_stack0_ = L_stack0_ 2025-03-04T21:02:40.1523973Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:02:40.1524463Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:02:40.1525707Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:02:40.1526213Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:02:40.1526753Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:02:40.1527306Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:02:40.1527856Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:02:40.1528952Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:02:40.1529750Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:02:40.1530181Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:02:40.1530595Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:02:40.1530997Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:02:40.1531292Z 2025-03-04T21:02:40.1531673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:02:40.1532150Z x: "f32[3225, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:02:40.1532911Z x_1: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:02:40.1533644Z x_2: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:02:40.1534361Z x_3: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:02:40.1535137Z x_4: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:02:40.1535451Z 2025-03-04T21:02:40.1535861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:02:40.1536900Z scores: "f32[3225, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:02:40.1537659Z 2025-03-04T21:02:40.1538083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:02:40.1539116Z proposal_deltas: "f32[3225, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:02:40.1539865Z 2025-03-04T21:02:40.1540257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:02:40.1540735Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:02:40.1541003Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:02:40.1541248Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:02:40.1541546Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:02:40.1541820Z getitem_2: "Sym(1225 - s0)" = size_1[0] 2025-03-04T21:02:40.1542069Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:02:40.1542298Z 2025-03-04T21:02:40.1542687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:02:40.1543667Z proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:02:40.1544409Z 2025-03-04T21:02:40.1544874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:02:40.1545452Z deltas: "f32[3225, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:02:40.1545730Z 2025-03-04T21:02:40.1546132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:02:40.1546666Z boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:02:40.1546948Z 2025-03-04T21:02:40.1547375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:02:40.1547886Z getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:02:40.1548197Z getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:40.1548520Z widths: "f32[3225][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:02:40.1548785Z 2025-03-04T21:02:40.1549197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:02:40.1549696Z getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:02:40.1550017Z getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:02:40.1550339Z heights: "f32[3225][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:02:40.1550613Z 2025-03-04T21:02:40.1551023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:02:40.1551518Z getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:40.1551782Z mul: "f32[3225][1]cpu" = 0.5 * widths 2025-03-04T21:02:40.1552048Z ctr_x: "f32[3225][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:02:40.1552292Z 2025-03-04T21:02:40.1552699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:02:40.1553215Z getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:02:40.1553509Z mul_1: "f32[3225][1]cpu" = 0.5 * heights 2025-03-04T21:02:40.1553781Z ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:02:40.1554032Z 2025-03-04T21:02:40.1554454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:02:40.1554979Z getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:02:40.1555314Z dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:02:40.1555558Z 2025-03-04T21:02:40.1555953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:02:40.1556465Z getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:02:40.1556812Z dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:02:40.1557063Z 2025-03-04T21:02:40.1557446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:02:40.1557964Z getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:02:40.1558276Z dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:02:40.1558505Z 2025-03-04T21:02:40.1558886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:02:40.1559410Z getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:02:40.1559755Z dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:02:40.1559985Z 2025-03-04T21:02:40.1560401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:02:40.1560937Z dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:02:40.1561192Z 2025-03-04T21:02:40.1561596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:02:40.1562117Z dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:02:40.1562360Z 2025-03-04T21:02:40.1562785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:02:40.1563341Z getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:02:40.1563655Z mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:02:40.1563980Z getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:02:40.1564320Z pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:02:40.1564573Z 2025-03-04T21:02:40.1565003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:02:40.1565537Z getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:02:40.1565844Z mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:02:40.1566168Z getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:02:40.1566499Z pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:02:40.1566755Z 2025-03-04T21:02:40.1567170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:02:40.1567669Z exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:02:40.1567987Z getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:02:40.1568321Z pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:02:40.1568568Z 2025-03-04T21:02:40.1568991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:02:40.1569522Z exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:02:40.1569873Z getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:02:40.1570224Z pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:02:40.1570482Z 2025-03-04T21:02:40.1570890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:02:40.1571359Z mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:02:40.1571629Z x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:02:40.1571867Z 2025-03-04T21:02:40.1572268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:02:40.1572732Z mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:02:40.1572993Z y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:02:40.1573232Z 2025-03-04T21:02:40.1573646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:02:40.1574126Z mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:02:40.1574419Z x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:02:40.1574668Z 2025-03-04T21:02:40.1575137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:02:40.1575622Z mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:02:40.1575938Z y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:02:40.1576194Z 2025-03-04T21:02:40.1576627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:02:40.1577228Z pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:02:40.1577525Z 2025-03-04T21:02:40.1577941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:02:40.1578499Z predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320)); pred_boxes = None 2025-03-04T21:02:40.1578790Z 2025-03-04T21:02:40.1579240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:02:40.1579857Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:02:40.1580223Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:02:40.1580516Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:02:40.1580822Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:02:40.1581140Z getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:02:40.1581404Z 2025-03-04T21:02:40.1581780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:02:40.1582336Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:02:40.1582691Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:02:40.1582956Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:02:40.1583355Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:02:40.1583715Z getitem_26: "Sym(1225 - s0)" = size_3[0] 2025-03-04T21:02:40.1583965Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:02:40.1584186Z 2025-03-04T21:02:40.1584612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:02:40.1585168Z probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:02:40.1585459Z 2025-03-04T21:02:40.1585904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:02:40.1586499Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:02:40.1586857Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:02:40.1587165Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:02:40.1587466Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:02:40.1587775Z getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:02:40.1588035Z 2025-03-04T21:02:40.1588748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:02:40.1589479Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:02:40.1589818Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:02:40.1590155Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:02:40.1590495Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:02:40.1590791Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:02:40.1591032Z 2025-03-04T21:02:40.1591468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:02:40.1591978Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:02:40.1592212Z 2025-03-04T21:02:40.1592346Z 2025-03-04T21:02:40.1592439Z class GraphModule(torch.nn.Module): 2025-03-04T21:02:40.1594294Z def forward(self, L_stack0_: "f32[3225, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"): 2025-03-04T21:02:40.1596319Z l_stack0_ = L_stack0_ 2025-03-04T21:02:40.1596664Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:02:40.1597171Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:02:40.1597644Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:02:40.1598114Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:02:40.1598634Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:02:40.1599203Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:02:40.1599779Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:02:40.1600353Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:02:40.1600822Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:02:40.1601219Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:02:40.1601612Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:02:40.1601999Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:02:40.1602287Z 2025-03-04T21:02:40.1602654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:02:40.1603138Z x: "f32[3225, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:02:40.1603833Z x_1: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:02:40.1604538Z x_2: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:02:40.1605249Z x_3: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:02:40.1605950Z x_4: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:02:40.1606233Z 2025-03-04T21:02:40.1606633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:02:40.1607588Z scores: "f32[3225, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:02:40.1608287Z 2025-03-04T21:02:40.1608697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:02:40.1609692Z proposal_deltas: "f32[3225, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:02:40.1610426Z 2025-03-04T21:02:40.1610799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:02:40.1611254Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:02:40.1611509Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:02:40.1611742Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:02:40.1612017Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:02:40.1612275Z getitem_2: "Sym(1225 - s0)" = size_1[0] 2025-03-04T21:02:40.1612514Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:02:40.1612743Z 2025-03-04T21:02:40.1613111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:02:40.1614044Z proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:02:40.1614752Z 2025-03-04T21:02:40.1615306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:02:40.1615922Z deltas: "f32[3225, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:02:40.1616209Z 2025-03-04T21:02:40.1616641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:02:40.1617204Z boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:02:40.1617503Z 2025-03-04T21:02:40.1617946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:02:40.1618480Z getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:02:40.1618810Z getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:40.1619150Z widths: "f32[3225][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:02:40.1619434Z 2025-03-04T21:02:40.1619866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:02:40.1620402Z getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:02:40.1620711Z getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:02:40.1621047Z heights: "f32[3225][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:02:40.1621327Z 2025-03-04T21:02:40.1621754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:02:40.1622272Z getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:40.1622544Z mul: "f32[3225][1]cpu" = 0.5 * widths 2025-03-04T21:02:40.1622817Z ctr_x: "f32[3225][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:02:40.1623071Z 2025-03-04T21:02:40.1623494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:02:40.1624053Z getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:02:40.1624374Z mul_1: "f32[3225][1]cpu" = 0.5 * heights 2025-03-04T21:02:40.1624656Z ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:02:40.1624914Z 2025-03-04T21:02:40.1625338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:02:40.1625840Z getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:02:40.1626163Z dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:02:40.1626395Z 2025-03-04T21:02:40.1626778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:02:40.1627280Z getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:02:40.1627598Z dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:02:40.1627829Z 2025-03-04T21:02:40.1628229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:02:40.1628727Z getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:02:40.1629046Z dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:02:40.1629277Z 2025-03-04T21:02:40.1629660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:02:40.1630211Z getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:02:40.1630552Z dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:02:40.1630783Z 2025-03-04T21:02:40.1631205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:02:40.1631726Z dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:02:40.1631980Z 2025-03-04T21:02:40.1632392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:02:40.1632903Z dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:02:40.1633155Z 2025-03-04T21:02:40.1633579Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:02:40.1634104Z getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:02:40.1634418Z mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:02:40.1634745Z getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:02:40.1635086Z pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:02:40.1635341Z 2025-03-04T21:02:40.1635773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:02:40.1636301Z getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:02:40.1636649Z mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:02:40.1636972Z getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:02:40.1637328Z pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:02:40.1637578Z 2025-03-04T21:02:40.1637987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:02:40.1638478Z exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:02:40.1638801Z getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:02:40.1639137Z pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:02:40.1639385Z 2025-03-04T21:02:40.1639800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:02:40.1640294Z exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:02:40.1640638Z getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:02:40.1640984Z pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:02:40.1641237Z 2025-03-04T21:02:40.1641635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:02:40.1642091Z mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:02:40.1642354Z x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:02:40.1642609Z 2025-03-04T21:02:40.1643001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:02:40.1643453Z mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:02:40.1643714Z y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:02:40.1643944Z 2025-03-04T21:02:40.1644332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:02:40.1644799Z mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:02:40.1645088Z x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:02:40.1645334Z 2025-03-04T21:02:40.1645720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:02:40.1646188Z mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:02:40.1646473Z y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:02:40.1646716Z 2025-03-04T21:02:40.1647143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:02:40.1647720Z pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:02:40.1648012Z 2025-03-04T21:02:40.1648433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:02:40.1648982Z predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320)); pred_boxes = None 2025-03-04T21:02:40.1649269Z 2025-03-04T21:02:40.1649740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:02:40.1650366Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:02:40.1650733Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:02:40.1651025Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:02:40.1651335Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:02:40.1651658Z getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:02:40.1651920Z 2025-03-04T21:02:40.1652297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:02:40.1652861Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:02:40.1653210Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:02:40.1653446Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:02:40.1653832Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:02:40.1654190Z getitem_26: "Sym(1225 - s0)" = size_3[0] 2025-03-04T21:02:40.1654443Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:02:40.1654666Z 2025-03-04T21:02:40.1655180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:02:40.1655781Z probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:02:40.1656118Z 2025-03-04T21:02:40.1656573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:02:40.1657162Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:02:40.1657517Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:02:40.1657804Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:02:40.1658102Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:02:40.1658413Z getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:02:40.1658668Z 2025-03-04T21:02:40.1659209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:02:40.1659885Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:02:40.1660222Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:02:40.1660551Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:02:40.1660888Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:02:40.1661177Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:02:40.1661410Z 2025-03-04T21:02:40.1661847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:02:40.1662351Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:02:40.1662583Z 2025-03-04T21:02:42.1574300Z 2025-03-04T21:02:42.1575535Z class GraphModule(torch.nn.Module): 2025-03-04T21:02:42.1576714Z def forward(self, L_predictions_0_: "f32[3225, 81][81, 1]cpu", L_predictions_1_: "f32[3225, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"): 2025-03-04T21:02:42.1578138Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:02:42.1578384Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:02:42.1578712Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:02:42.1579132Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:02:42.1579567Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:02:42.1579974Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:02:42.1580282Z 2025-03-04T21:02:42.1580777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:02:42.1581328Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:02:42.1581597Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:02:42.1581847Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:02:42.1582135Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:02:42.1582410Z getitem_2: "Sym(1225 - s0)" = size_1[0] 2025-03-04T21:02:42.1582670Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:02:42.1582946Z 2025-03-04T21:02:42.1583377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:02:42.1584354Z proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:02:42.1585105Z 2025-03-04T21:02:42.1585570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:02:42.1586139Z deltas: "f32[3225, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:02:42.1586413Z 2025-03-04T21:02:42.1586812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:02:42.1587339Z boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:02:42.1587616Z 2025-03-04T21:02:42.1588014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:02:42.1588718Z getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:02:42.1589023Z getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:42.1589336Z widths: "f32[3225][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:02:42.1589596Z 2025-03-04T21:02:42.1589997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:02:42.1590487Z getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:02:42.1590807Z getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:02:42.1591140Z heights: "f32[3225][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:02:42.1591397Z 2025-03-04T21:02:42.1591787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:02:42.1592262Z getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:42.1592514Z mul: "f32[3225][1]cpu" = 0.5 * widths 2025-03-04T21:02:42.1594031Z ctr_x: "f32[3225][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:02:42.1594317Z 2025-03-04T21:02:42.1594735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:02:42.1595258Z getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:02:42.1595546Z mul_1: "f32[3225][1]cpu" = 0.5 * heights 2025-03-04T21:02:42.1595811Z ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:02:42.1596108Z 2025-03-04T21:02:42.1596536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:02:42.1597045Z getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:02:42.1597373Z dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:02:42.1597602Z 2025-03-04T21:02:42.1597991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:02:42.1598532Z getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:02:42.1598855Z dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:02:42.1599091Z 2025-03-04T21:02:42.1599473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:02:42.1599967Z getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:02:42.1600284Z dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:02:42.1600515Z 2025-03-04T21:02:42.1600899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:02:42.1601436Z getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:02:42.1601785Z dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:02:42.1602017Z 2025-03-04T21:02:42.1602439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:02:42.1602962Z dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:02:42.1603218Z 2025-03-04T21:02:42.1605419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:02:42.1605987Z dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:02:42.1606243Z 2025-03-04T21:02:42.1606743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:02:42.1607281Z getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:02:42.1607630Z mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:02:42.1607970Z getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:02:42.1608326Z pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:02:42.1608593Z 2025-03-04T21:02:42.1609677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:02:42.1610235Z getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:02:42.1610559Z mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:02:42.1610894Z getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:02:42.1611560Z pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:02:42.1611839Z 2025-03-04T21:02:42.1612305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:02:42.1612819Z exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:02:42.1613158Z getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:02:42.1613508Z pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:02:42.1614102Z 2025-03-04T21:02:42.1614549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:02:42.1616663Z exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:02:42.1617027Z getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:02:42.1617393Z pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:02:42.1617658Z 2025-03-04T21:02:42.1618076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:02:42.1618555Z mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:02:42.1618828Z x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:02:42.1619074Z 2025-03-04T21:02:42.1619486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:02:42.1619966Z mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:02:42.1620233Z y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:02:42.1621108Z 2025-03-04T21:02:42.1621516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:02:42.1622008Z mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:02:42.1622309Z x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:02:42.1622566Z 2025-03-04T21:02:42.1622967Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:02:42.1623456Z mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:02:42.1623783Z y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:02:42.1624056Z 2025-03-04T21:02:42.1624500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:02:42.1625105Z pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:02:42.1625402Z 2025-03-04T21:02:42.1625836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:02:42.1626400Z predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320)); pred_boxes = None 2025-03-04T21:02:42.1626695Z 2025-03-04T21:02:42.1627159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:02:42.1627788Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:02:42.1628173Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:02:42.1628468Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:02:42.1628773Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:02:42.1629094Z getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:02:42.1629365Z 2025-03-04T21:02:42.1629737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:02:42.1630309Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:02:42.1630654Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:02:42.1630902Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:02:42.1631269Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:02:42.1631619Z getitem_26: "Sym(1225 - s0)" = size_3[0] 2025-03-04T21:02:42.1631864Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:02:42.1632080Z 2025-03-04T21:02:42.1632495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:02:42.1633072Z probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:02:42.1633393Z 2025-03-04T21:02:42.1633826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:02:42.1634415Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:02:42.1634773Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:02:42.1635058Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:02:42.1635354Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:02:42.1635667Z getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:02:42.1635924Z 2025-03-04T21:02:42.1636462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:02:42.1637183Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:02:42.1637546Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:02:42.1637878Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:02:42.1638214Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:02:42.1638507Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:02:42.1638759Z 2025-03-04T21:02:42.1639225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:02:42.1639767Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:02:42.1640012Z 2025-03-04T21:02:42.1640156Z 2025-03-04T21:02:42.1640269Z class GraphModule(torch.nn.Module): 2025-03-04T21:02:42.1641106Z def forward(self, L_predictions_0_: "f32[3225, 81][81, 1]cpu", L_predictions_1_: "f32[3225, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"): 2025-03-04T21:02:42.1641886Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:02:42.1642604Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:02:42.1642966Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:02:42.1643379Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:02:42.1643775Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:02:42.1644196Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:02:42.1644485Z 2025-03-04T21:02:42.1644876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:02:42.1645335Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:02:42.1645590Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:02:42.1645822Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:02:42.1646092Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:02:42.1646350Z getitem_2: "Sym(1225 - s0)" = size_1[0] 2025-03-04T21:02:42.1646592Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:02:42.1646808Z 2025-03-04T21:02:42.1647164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:02:42.1648094Z proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:02:42.1648795Z 2025-03-04T21:02:42.1649243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:02:42.1649799Z deltas: "f32[3225, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:02:42.1650068Z 2025-03-04T21:02:42.1650460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:02:42.1650992Z boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:02:42.1651293Z 2025-03-04T21:02:42.1651688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:02:42.1652186Z getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:02:42.1652490Z getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:42.1652812Z widths: "f32[3225][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:02:42.1653087Z 2025-03-04T21:02:42.1653509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:02:42.1654505Z getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:02:42.1654876Z getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:02:42.1655595Z heights: "f32[3225][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:02:42.1655897Z 2025-03-04T21:02:42.1656359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:02:42.1656910Z getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:42.1657206Z mul: "f32[3225][1]cpu" = 0.5 * widths 2025-03-04T21:02:42.1657473Z ctr_x: "f32[3225][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:02:42.1657726Z 2025-03-04T21:02:42.1658150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:02:42.1658696Z getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:02:42.1658997Z mul_1: "f32[3225][1]cpu" = 0.5 * heights 2025-03-04T21:02:42.1659273Z ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:02:42.1659528Z 2025-03-04T21:02:42.1659961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:02:42.1660502Z getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:02:42.1660835Z dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:02:42.1661077Z 2025-03-04T21:02:42.1661476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:02:42.1662006Z getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:02:42.1662346Z dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:02:42.1662593Z 2025-03-04T21:02:42.1662981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:02:42.1663494Z getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:02:42.1663820Z dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:02:42.1664053Z 2025-03-04T21:02:42.1664440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:02:42.1664986Z getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:02:42.1665333Z dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:02:42.1665585Z 2025-03-04T21:02:42.1666035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:02:42.1666579Z dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:02:42.1666851Z 2025-03-04T21:02:42.1667286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:02:42.1667826Z dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:02:42.1668086Z 2025-03-04T21:02:42.1668525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:02:42.1669088Z getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:02:42.1669413Z mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:02:42.1669769Z getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:02:42.1670124Z pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:02:42.1670378Z 2025-03-04T21:02:42.1670816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:02:42.1671362Z getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:02:42.1671682Z mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:02:42.1672032Z getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:02:42.1672374Z pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:02:42.1672637Z 2025-03-04T21:02:42.1673060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:02:42.1673563Z exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:02:42.1673887Z getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:02:42.1674231Z pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:02:42.1674484Z 2025-03-04T21:02:42.1674907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:02:42.1675410Z exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:02:42.1675743Z getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:02:42.1676095Z pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:02:42.1676352Z 2025-03-04T21:02:42.1676756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:02:42.1677218Z mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:02:42.1677813Z x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:02:42.1678065Z 2025-03-04T21:02:42.1678466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:02:42.1678974Z mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:02:42.1679237Z y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:02:42.1679492Z 2025-03-04T21:02:42.1679897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:02:42.1680360Z mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:02:42.1680646Z x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:02:42.1680890Z 2025-03-04T21:02:42.1681274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:02:42.1681735Z mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:02:42.1682020Z y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:02:42.1682263Z 2025-03-04T21:02:42.1682707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:02:42.1683284Z pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:02:42.1683572Z 2025-03-04T21:02:42.1683990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:02:42.1684538Z predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320)); pred_boxes = None 2025-03-04T21:02:42.1684823Z 2025-03-04T21:02:42.1685266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:02:42.1685910Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:02:42.1686272Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:02:42.1686561Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:02:42.1686855Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:02:42.1687165Z getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:02:42.1687422Z 2025-03-04T21:02:42.1687797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:02:42.1688511Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:02:42.1688870Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:02:42.1689125Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:02:42.1689501Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:02:42.1689867Z getitem_26: "Sym(1225 - s0)" = size_3[0] 2025-03-04T21:02:42.1690125Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:02:42.1690350Z 2025-03-04T21:02:42.1690786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:02:42.1691399Z probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:02:42.1691735Z 2025-03-04T21:02:42.1692282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:02:42.1692894Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:02:42.1693296Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:02:42.1693601Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:02:42.1694366Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:02:42.1694699Z getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:02:42.1695486Z 2025-03-04T21:02:42.1696096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:02:42.1696830Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:02:42.1697181Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:02:42.1697531Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:02:42.1697938Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:02:42.1698262Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:02:42.1698521Z 2025-03-04T21:02:42.1698994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:02:42.1699551Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:02:42.1699803Z 2025-03-04T21:02:42.1699982Z 2025-03-04T21:02:42.1700078Z class GraphModule(torch.nn.Module): 2025-03-04T21:02:42.1700942Z def forward(self, L_predictions_0_: "f32[3225, 81][81, 1]cpu", L_predictions_1_: "f32[3225, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"): 2025-03-04T21:02:42.1701781Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:02:42.1702028Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:02:42.1702362Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:02:42.1702792Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:02:42.1703216Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:02:42.1703636Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:02:42.1703951Z 2025-03-04T21:02:42.1704361Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:02:42.1704857Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:02:42.1705130Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:02:42.1705382Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:02:42.1705676Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:02:42.1708530Z getitem_2: "Sym(1225 - s0)" = size_1[0] 2025-03-04T21:02:42.1709404Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:02:42.1709654Z 2025-03-04T21:02:42.1710057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:02:42.1711120Z proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:02:42.1711883Z 2025-03-04T21:02:42.1712358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:02:42.1712943Z deltas: "f32[3225, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:02:42.1713221Z 2025-03-04T21:02:42.1713630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:02:42.1714163Z boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:02:42.1714449Z 2025-03-04T21:02:42.1714857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:02:42.1715385Z getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:02:42.1715696Z getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:42.1716019Z widths: "f32[3225][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:02:42.1716286Z 2025-03-04T21:02:42.1716698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:02:42.1717192Z getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:02:42.1717513Z getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:02:42.1717835Z heights: "f32[3225][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:02:42.1718101Z 2025-03-04T21:02:42.1718502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:02:42.1718989Z getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:02:42.1719253Z mul: "f32[3225][1]cpu" = 0.5 * widths 2025-03-04T21:02:42.1719513Z ctr_x: "f32[3225][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:02:42.1719747Z 2025-03-04T21:02:42.1720152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:02:42.1720656Z getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:02:42.1720949Z mul_1: "f32[3225][1]cpu" = 0.5 * heights 2025-03-04T21:02:42.1721217Z ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:02:42.1721461Z 2025-03-04T21:02:42.1721878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:02:42.1723156Z getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:02:42.1723493Z dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:02:42.1723733Z 2025-03-04T21:02:42.1724132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:02:42.1724648Z getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:02:42.1725357Z dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:02:42.1726450Z 2025-03-04T21:02:42.1726865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:02:42.1727377Z getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:02:42.1727709Z dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:02:42.1727944Z 2025-03-04T21:02:42.1728337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:02:42.1728882Z getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:02:42.1729234Z dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:02:42.1729486Z 2025-03-04T21:02:42.1729921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:02:42.1730491Z dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:02:42.1730753Z 2025-03-04T21:02:42.1731177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:02:42.1731700Z dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:02:42.1731956Z 2025-03-04T21:02:42.1732396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:02:42.1732961Z getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:02:42.1733279Z mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:02:42.1733619Z getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:02:42.1733965Z pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:02:42.1734228Z 2025-03-04T21:02:42.1734676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:02:42.1735335Z getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:02:42.1735670Z mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:02:42.1736020Z getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:02:42.1736381Z pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:02:42.1736657Z 2025-03-04T21:02:42.1737105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:02:42.1737638Z exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:02:42.1737978Z getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:02:42.1738351Z pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:02:42.1738628Z 2025-03-04T21:02:42.1739097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:02:42.1739695Z exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:02:42.1740055Z getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:02:42.1740450Z pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:02:42.1740721Z 2025-03-04T21:02:42.1741151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:02:42.1741644Z mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:02:42.1741919Z x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:02:42.1742243Z 2025-03-04T21:02:42.1742675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:02:42.1743171Z mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:02:42.1743449Z y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:02:42.1743701Z 2025-03-04T21:02:42.1744163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:02:42.1744675Z mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:02:42.1744988Z x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:02:42.1745253Z 2025-03-04T21:02:42.1745668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:02:42.1746168Z mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:02:42.1746491Z y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:02:42.1746765Z 2025-03-04T21:02:42.1747192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:02:42.1747766Z pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:02:42.1748055Z 2025-03-04T21:02:42.1748467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:02:42.1749004Z predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320)); pred_boxes = None 2025-03-04T21:02:42.1749285Z 2025-03-04T21:02:42.1749718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:02:42.1750316Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:02:42.1750675Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:02:42.1750963Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:02:42.1751264Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:02:42.1751574Z getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:02:42.1751831Z 2025-03-04T21:02:42.1752209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:02:42.1752756Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:02:42.1753096Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:02:42.1753356Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:02:42.1753720Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:02:42.1754091Z getitem_26: "Sym(1225 - s0)" = size_3[0] 2025-03-04T21:02:42.1754336Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:02:42.1754553Z 2025-03-04T21:02:42.1754974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:02:42.1755563Z probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:02:42.1756628Z 2025-03-04T21:02:42.1757136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:02:42.1757750Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:02:42.1758103Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:02:42.1758425Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:02:42.1758725Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:02:42.1759044Z getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:02:42.1759306Z 2025-03-04T21:02:42.1760611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:02:42.1762046Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:02:42.1762405Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:02:42.1762749Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:02:42.1763101Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:02:42.1763403Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:02:42.1763650Z 2025-03-04T21:02:42.1764111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:02:42.1764638Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:02:42.1764877Z 2025-03-04T21:02:44.9733075Z 2025-03-04T21:02:44.9737473Z class GraphModule(torch.nn.Module): 2025-03-04T21:02:44.9737918Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T21:02:44.9738235Z l_scores_0_ = L_scores_0_ 2025-03-04T21:02:44.9738458Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:02:44.9738657Z 2025-03-04T21:02:44.9739270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:02:44.9739974Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:02:44.9740309Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:02:44.9740643Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:02:44.9740969Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:02:44.9741272Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:02:44.9741809Z 2025-03-04T21:02:44.9742282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:02:44.9742878Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:02:44.9743126Z 2025-03-04T21:02:44.9743226Z 2025-03-04T21:02:44.9743321Z class GraphModule(torch.nn.Module): 2025-03-04T21:02:44.9743634Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T21:02:44.9743941Z l_scores_0_ = L_scores_0_ 2025-03-04T21:02:44.9744157Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:02:44.9744352Z 2025-03-04T21:02:44.9744911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:02:44.9745584Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:02:44.9745915Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:02:44.9746278Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:02:44.9746600Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:02:44.9746895Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:02:44.9747135Z 2025-03-04T21:02:44.9747586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:02:44.9748122Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:02:44.9748413Z 2025-03-04T21:03:02.0023002Z Compilation time (from dynamo_timed): 47.378132943 2025-03-04T21:03:02.0023351Z pass 2025-03-04T21:03:02.0027727Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:03:02.0029232Z TIMING: entire_frame_compile:47.37813 gc:0.03874 _recursive_pre_grad_passes:0.03282 async_compile.wait:11.16883 backend_compile:31.79273 _recursive_joint_graph_passes:0.56571 _recursive_post_grad_passes:0.14279 code_gen:16.50607 inductor_compile:19.13018 total_wall_time:47.37813 2025-03-04T21:03:02.0030697Z STATS: call_* op count: 781 | FakeTensorMode.__torch_dispatch__:27911 | FakeTensor.__torch_dispatch__:3329 | ProxyTorchDispatchMode.__torch_dispatch__:10376 | attempt fast:51 | slow no contiguity match:20 | fast is_contiguous:31 2025-03-04T21:03:02.0031505Z Dynamo produced 52 graphs covering 781 ops with 42 graph breaks (6 unique) 2025-03-04T21:03:08.1829101Z 2025-03-04T21:03:15.7164851Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:03:15.7168038Z loading model: 0it [00:07, ?it/s] 2025-03-04T21:03:15.7177305Z cpu eval detectron2_fasterrcnn_r_101_fpn 2025-03-04T21:03:33.3376757Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_101_fpn. Setting accuracy check to cosine 2025-03-04T21:03:33.3590528Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:03:41.8497448Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:03:49.9022111Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:04:05.4278122Z 2025-03-04T21:04:05.4283021Z class GraphModule(torch.nn.Module): 2025-03-04T21:04:05.4414031Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", 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L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:04:05.4572358Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:04:05.4573022Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4574011Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4575421Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4576489Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4577440Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4578378Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4579332Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4580423Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4581412Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4582349Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4583267Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4584273Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4585246Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4586191Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4587105Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4587983Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4588952Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4589837Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4590708Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4591582Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4592418Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:04:05.4593240Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4594111Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4594968Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4595859Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4596642Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4597440Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4598316Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4599153Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4599976Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4600742Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4601552Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4602401Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4624220Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4625238Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4626217Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4627105Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4628026Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4628874Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4629695Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4630509Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4631318Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4632186Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4633021Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4633861Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4634634Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4635432Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4636288Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4637123Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4637950Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4638744Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4639581Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4640465Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4641337Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4642155Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4642939Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4643767Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4644661Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4645513Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4646331Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4647142Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4647957Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4648822Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4649663Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4650484Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4651270Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4652070Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4652934Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4653802Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4654853Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4655726Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:04:05.4656582Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4657502Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4658412Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4659266Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4660071Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4660885Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4661782Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4662640Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4663467Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4664259Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4665091Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4665958Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4666786Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4667590Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4668386Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4669237Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4670093Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4670925Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4671729Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4672518Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4673317Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4674171Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4675036Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4675847Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4676628Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4677435Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4678291Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4679133Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4679939Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4680708Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4681531Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4682390Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4683242Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4684041Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4684803Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4685607Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4686470Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4687297Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4688308Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4689218Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4690053Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4690930Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4691783Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4692610Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4693409Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4694285Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4695205Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4696136Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4696993Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4697793Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4698620Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4699508Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4700421Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4701249Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4702057Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4702890Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4703801Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4704661Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4705498Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4706318Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4707135Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4707996Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4708848Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4709667Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4710510Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:04:05.4711368Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4712246Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4713106Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4713941Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4714745Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4715562Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4716415Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4717266Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4718079Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4718846Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4719646Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4720496Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4721324Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4722133Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4722911Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4723731Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4724617Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4725458Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4726272Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4727055Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4727894Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4728749Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4729592Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4730389Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4731189Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4731993Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4732851Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4733684Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4734550Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4735362Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4736313Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4737233Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4738125Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4738971Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4739772Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4740612Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4741514Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4742407Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4743250Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4744058Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4744923Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4745826Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4746697Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4747536Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4748343Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4749181Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4750083Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4750954Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4751784Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4752567Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4753375Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4754226Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4755047Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4755862Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4756628Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4757427Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4758277Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4759126Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4759935Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4760712Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4761519Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4762381Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4763214Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4764016Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4764788Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4765616Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4766484Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4767321Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4768134Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4768911Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4769731Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4770587Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4771429Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4772250Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4773025Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4773864Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4774858Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4775746Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4776614Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4777450Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4778317Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4779254Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4780193Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4781086Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4781922Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4782797Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4783749Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4784696Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4785554Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4786321Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4787145Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4788006Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4789004Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4789827Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4790619Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4791439Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4792292Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4793121Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4793977Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4794769Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4795566Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4796419Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4797248Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4798079Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4798862Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4799673Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4800559Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4801421Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4802233Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4803018Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4803821Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4804688Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4805528Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4806338Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4807118Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4807944Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4808811Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4809646Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4810455Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4811231Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4812061Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4812915Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4813772Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4814830Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4815731Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4816675Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4817670Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4818647Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4819610Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4820533Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4821493Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4822520Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4823511Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4824432Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4825211Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4826008Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4826864Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4827714Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4828516Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4829290Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4830126Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4830997Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4831835Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4832656Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4833444Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4834264Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4835132Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4835972Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4836815Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4837600Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4838408Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4839285Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4840137Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4840968Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4841744Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4842544Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4843416Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4844242Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4845052Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4845826Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4846627Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4847488Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4848321Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4849126Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4849913Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4850723Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4851594Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4852423Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4853226Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4853997Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4854884Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4855867Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4856733Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4857582Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4858383Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4859214Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4860085Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4860938Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4861750Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4862525Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4863333Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4864213Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4865080Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4865885Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4866655Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4867460Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4868336Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4869175Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4869990Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4870780Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4871594Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4872457Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4873287Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4874096Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4874873Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4875677Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4876532Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4877403Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4897178Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4898195Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4899017Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4899896Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4900858Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4901688Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4902467Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4903271Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4904173Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4905020Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4905835Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4906618Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4907424Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4908280Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4909119Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4909923Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4910749Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4911589Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4912455Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4913289Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4914107Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4914900Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4915717Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4916579Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4917428Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4918238Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4919013Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4919815Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4920676Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4921509Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4922313Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4923082Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4923906Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4924785Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4925629Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4926446Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4927228Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4928058Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4928916Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4929755Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4930553Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4931376Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4932176Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4933025Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4933854Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4934767Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4935585Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4936435Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4937313Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4938209Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4939087Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4939915Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4940777Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4941691Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4942591Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4943450Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4944274Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4945133Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4946035Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4946915Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4947728Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4948504Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4949300Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4950142Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4950969Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4951797Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4952586Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4953390Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4954251Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4955085Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4955917Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4956693Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4957489Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4958348Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4959593Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4960409Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4961180Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4961992Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4962861Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4963704Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4964515Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4965289Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4966120Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4967002Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4967831Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4968633Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4969413Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4970235Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4971091Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4971935Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4972779Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4973572Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4974465Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4975375Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4976281Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4977128Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4977924Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4978752Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4979661Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4980530Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4981362Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4982150Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4982972Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4983867Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4984711Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4985527Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4986313Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4987153Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4988033Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4989008Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4989851Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4990644Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.4991472Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4992353Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4993215Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4994079Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4994894Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.4995731Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.4996604Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.4997467Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.4998330Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.4999136Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.4999945Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5000845Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5001671Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5002469Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5003235Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.5004032Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5004887Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5005711Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5006518Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5007301Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.5008129Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5009012Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5009868Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5010706Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5011518Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.5012337Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5013214Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5014068Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5015051Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5015887Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.5016706Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5017576Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5018422Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5019235Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5020019Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.5020833Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5021725Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5022597Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5023420Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5024212Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:04:05.5025064Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5025981Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5026867Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5027722Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5028537Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.5029362Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5030232Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5031084Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5031910Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5032696Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.5033512Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5034389Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5035250Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5036088Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5036879Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.5037696Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5038573Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5039475Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5040318Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5041121Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.5041933Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5042819Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5043675Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5044499Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5045290Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.5046114Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5046990Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5047841Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5048668Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5049472Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.5050309Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5051193Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5052045Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5052875Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5053571Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T21:04:05.5054098Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T21:04:05.5054684Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T21:04:05.5055218Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T21:04:05.5055752Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T21:04:05.5056302Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T21:04:05.5056799Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T21:04:05.5057291Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T21:04:05.5057785Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T21:04:05.5058275Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T21:04:05.5058770Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T21:04:05.5059263Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T21:04:05.5059757Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T21:04:05.5060252Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T21:04:05.5060741Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T21:04:05.5061228Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T21:04:05.5061854Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:04:05.5062615Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:04:05.5063411Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:04:05.5064182Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:04:05.5064930Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:04:05.5065658Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:04:05.5066352Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:04:05.5067101Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:04:05.5067921Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:04:05.5068699Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:04:05.5069458Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:04:05.5069957Z 2025-03-04T21:04:05.5070372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5071294Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5071966Z 2025-03-04T21:04:05.5072355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5074535Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5076506Z 2025-03-04T21:04:05.5076915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:04:05.5077432Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:04:05.5077716Z 2025-03-04T21:04:05.5078230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:04:05.5078939Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:04:05.5079312Z 2025-03-04T21:04:05.5079690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5080533Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5081170Z 2025-03-04T21:04:05.5081547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5083807Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5085810Z 2025-03-04T21:04:05.5086213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5086737Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:04:05.5087017Z 2025-03-04T21:04:05.5087383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5088404Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5089066Z 2025-03-04T21:04:05.5089450Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5091791Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5093828Z 2025-03-04T21:04:05.5094279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5094796Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:04:05.5095097Z 2025-03-04T21:04:05.5095486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5096377Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5097003Z 2025-03-04T21:04:05.5097387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5099491Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5101407Z 2025-03-04T21:04:05.5101752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5102574Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:04:05.5103208Z 2025-03-04T21:04:05.5103570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5105764Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5107893Z 2025-03-04T21:04:05.5108268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5108759Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:04:05.5109030Z 2025-03-04T21:04:05.5109408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5109908Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:04:05.5110176Z 2025-03-04T21:04:05.5110506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5111316Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5111915Z 2025-03-04T21:04:05.5112263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5114353Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5116204Z 2025-03-04T21:04:05.5116566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5117042Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:04:05.5117305Z 2025-03-04T21:04:05.5117634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5118422Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5119014Z 2025-03-04T21:04:05.5119365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5121423Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5123278Z 2025-03-04T21:04:05.5123646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5124126Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:04:05.5124391Z 2025-03-04T21:04:05.5124728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5125542Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5126143Z 2025-03-04T21:04:05.5126491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5128550Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5130415Z 2025-03-04T21:04:05.5130778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5131268Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:04:05.5131540Z 2025-03-04T21:04:05.5131910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5132399Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:04:05.5132670Z 2025-03-04T21:04:05.5133010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5133800Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5134506Z 2025-03-04T21:04:05.5134936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5137240Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5139370Z 2025-03-04T21:04:05.5139849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5140416Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:04:05.5140728Z 2025-03-04T21:04:05.5141122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5142078Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5142788Z 2025-03-04T21:04:05.5143203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5145567Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5147391Z 2025-03-04T21:04:05.5147757Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5148227Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:04:05.5148487Z 2025-03-04T21:04:05.5148822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5149623Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5150240Z 2025-03-04T21:04:05.5150591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5152700Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5154530Z 2025-03-04T21:04:05.5154893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5155382Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:04:05.5155652Z 2025-03-04T21:04:05.5156019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5156528Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:04:05.5156800Z 2025-03-04T21:04:05.5157136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5157923Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5158515Z 2025-03-04T21:04:05.5158866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5160943Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5162793Z 2025-03-04T21:04:05.5163160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5163671Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:04:05.5163949Z 2025-03-04T21:04:05.5164286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5165075Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5165678Z 2025-03-04T21:04:05.5166029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5168117Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5169966Z 2025-03-04T21:04:05.5170335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5170821Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:04:05.5171086Z 2025-03-04T21:04:05.5171420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5172243Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5172916Z 2025-03-04T21:04:05.5173275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5175553Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5177605Z 2025-03-04T21:04:05.5177952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5178795Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:04:05.5179421Z 2025-03-04T21:04:05.5179780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5181998Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5184093Z 2025-03-04T21:04:05.5184467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5184980Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:04:05.5185257Z 2025-03-04T21:04:05.5185664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5186164Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:04:05.5186444Z 2025-03-04T21:04:05.5186790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5187597Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5188413Z 2025-03-04T21:04:05.5188780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5190977Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5192865Z 2025-03-04T21:04:05.5193238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5193720Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:04:05.5193983Z 2025-03-04T21:04:05.5194316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5195122Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5195727Z 2025-03-04T21:04:05.5196105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5198204Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5200061Z 2025-03-04T21:04:05.5200424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5200901Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:04:05.5201163Z 2025-03-04T21:04:05.5201497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5202292Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5202893Z 2025-03-04T21:04:05.5203240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5205313Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5207158Z 2025-03-04T21:04:05.5207511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5207993Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:04:05.5208266Z 2025-03-04T21:04:05.5208627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5209111Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:04:05.5209380Z 2025-03-04T21:04:05.5209724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5210534Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5211127Z 2025-03-04T21:04:05.5211480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5213647Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5215584Z 2025-03-04T21:04:05.5215964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5216456Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:04:05.5216719Z 2025-03-04T21:04:05.5217065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5217879Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5218489Z 2025-03-04T21:04:05.5218849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5220996Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5222907Z 2025-03-04T21:04:05.5223288Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5223794Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:04:05.5224067Z 2025-03-04T21:04:05.5224409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5225239Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5225875Z 2025-03-04T21:04:05.5226231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5228370Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5230220Z 2025-03-04T21:04:05.5230580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5231063Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:04:05.5231334Z 2025-03-04T21:04:05.5231694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5232176Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:04:05.5232446Z 2025-03-04T21:04:05.5232780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5233590Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5234198Z 2025-03-04T21:04:05.5234550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5236636Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5238478Z 2025-03-04T21:04:05.5238851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5239336Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:04:05.5239618Z 2025-03-04T21:04:05.5239954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5240755Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5241353Z 2025-03-04T21:04:05.5241703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5243830Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5245668Z 2025-03-04T21:04:05.5246038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5246522Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:04:05.5246783Z 2025-03-04T21:04:05.5247143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5247959Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5248566Z 2025-03-04T21:04:05.5248916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5251034Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5252857Z 2025-03-04T21:04:05.5253217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5253728Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:04:05.5254008Z 2025-03-04T21:04:05.5254492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5255033Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:04:05.5255329Z 2025-03-04T21:04:05.5255696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5256504Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5257114Z 2025-03-04T21:04:05.5257480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5259633Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5261561Z 2025-03-04T21:04:05.5261947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5262436Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:04:05.5262701Z 2025-03-04T21:04:05.5263046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5263852Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5264467Z 2025-03-04T21:04:05.5264832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5266993Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5268885Z 2025-03-04T21:04:05.5269260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5269743Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:04:05.5270003Z 2025-03-04T21:04:05.5270349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5271161Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5271771Z 2025-03-04T21:04:05.5272131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5274260Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5276155Z 2025-03-04T21:04:05.5276487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5277276Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:04:05.5277894Z 2025-03-04T21:04:05.5278260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5280407Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5282447Z 2025-03-04T21:04:05.5282816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5283292Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:04:05.5283550Z 2025-03-04T21:04:05.5283918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5284404Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:04:05.5284667Z 2025-03-04T21:04:05.5285007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5285791Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5286377Z 2025-03-04T21:04:05.5286728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5289108Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5291016Z 2025-03-04T21:04:05.5291388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5291862Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:04:05.5292120Z 2025-03-04T21:04:05.5292460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5293274Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5293875Z 2025-03-04T21:04:05.5294299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5296532Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5298546Z 2025-03-04T21:04:05.5298944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5299459Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:04:05.5299737Z 2025-03-04T21:04:05.5300098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5300943Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5301584Z 2025-03-04T21:04:05.5301959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5304254Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5306212Z 2025-03-04T21:04:05.5306584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5307073Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:04:05.5307347Z 2025-03-04T21:04:05.5307742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5308234Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:04:05.5308500Z 2025-03-04T21:04:05.5308842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5309638Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5310263Z 2025-03-04T21:04:05.5310617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5312757Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5314642Z 2025-03-04T21:04:05.5315018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5315503Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:04:05.5315769Z 2025-03-04T21:04:05.5316111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5316936Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5317545Z 2025-03-04T21:04:05.5317925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5320044Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5321870Z 2025-03-04T21:04:05.5322242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5322714Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:04:05.5322972Z 2025-03-04T21:04:05.5323308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5324097Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5324717Z 2025-03-04T21:04:05.5325067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5327111Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5328949Z 2025-03-04T21:04:05.5329310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5329782Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:04:05.5330048Z 2025-03-04T21:04:05.5330404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5330905Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:04:05.5331160Z 2025-03-04T21:04:05.5331511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5332288Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5332882Z 2025-03-04T21:04:05.5333224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5335410Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5337290Z 2025-03-04T21:04:05.5337664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5338162Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:04:05.5338421Z 2025-03-04T21:04:05.5338762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5339562Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5340170Z 2025-03-04T21:04:05.5340527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5342649Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5344543Z 2025-03-04T21:04:05.5344934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5345432Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:04:05.5345691Z 2025-03-04T21:04:05.5346030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5346825Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5347433Z 2025-03-04T21:04:05.5347790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5349914Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5351751Z 2025-03-04T21:04:05.5352115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5352599Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:04:05.5352862Z 2025-03-04T21:04:05.5353231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5353710Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:04:05.5353970Z 2025-03-04T21:04:05.5354316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5355097Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5355684Z 2025-03-04T21:04:05.5356035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5358099Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5359938Z 2025-03-04T21:04:05.5360305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5360775Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:04:05.5361028Z 2025-03-04T21:04:05.5361363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5362160Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5362750Z 2025-03-04T21:04:05.5363092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5365137Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5366977Z 2025-03-04T21:04:05.5367347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5367819Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:04:05.5368073Z 2025-03-04T21:04:05.5368410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5369194Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5369783Z 2025-03-04T21:04:05.5370133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5372234Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5374141Z 2025-03-04T21:04:05.5374595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5375124Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:04:05.5375414Z 2025-03-04T21:04:05.5375816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5376345Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:04:05.5376660Z 2025-03-04T21:04:05.5377005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5377851Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5378494Z 2025-03-04T21:04:05.5378872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5381140Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5383134Z 2025-03-04T21:04:05.5383531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5384051Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:04:05.5384328Z 2025-03-04T21:04:05.5384683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5385487Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5386092Z 2025-03-04T21:04:05.5386466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5388742Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5390638Z 2025-03-04T21:04:05.5391051Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5391542Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:04:05.5391804Z 2025-03-04T21:04:05.5392145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5392946Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5393579Z 2025-03-04T21:04:05.5393940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5396059Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5397976Z 2025-03-04T21:04:05.5398347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5398833Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:04:05.5399100Z 2025-03-04T21:04:05.5399479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5399968Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:04:05.5400240Z 2025-03-04T21:04:05.5400601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5401416Z x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5401994Z 2025-03-04T21:04:05.5402338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5404391Z x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5406275Z 2025-03-04T21:04:05.5406653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5407136Z out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:04:05.5407418Z 2025-03-04T21:04:05.5407761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5408562Z x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5409160Z 2025-03-04T21:04:05.5409526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5411660Z x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5413535Z 2025-03-04T21:04:05.5413910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5414497Z out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:04:05.5414773Z 2025-03-04T21:04:05.5415160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5415984Z x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5416594Z 2025-03-04T21:04:05.5416948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5419087Z x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5420993Z 2025-03-04T21:04:05.5421371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5421893Z x_93 += out_51; out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T21:04:05.5422166Z 2025-03-04T21:04:05.5422544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5423031Z out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:04:05.5423298Z 2025-03-04T21:04:05.5423639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5424445Z x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5425052Z 2025-03-04T21:04:05.5425413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5427551Z x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5429450Z 2025-03-04T21:04:05.5429817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5430293Z out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T21:04:05.5430549Z 2025-03-04T21:04:05.5430882Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5431664Z x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5432254Z 2025-03-04T21:04:05.5432617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5434662Z x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5436502Z 2025-03-04T21:04:05.5436866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5437331Z out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:04:05.5437584Z 2025-03-04T21:04:05.5437916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5438702Z x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5439309Z 2025-03-04T21:04:05.5439665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5441891Z x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5443805Z 2025-03-04T21:04:05.5444093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5444243Z x_99 += out_55; out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T21:04:05.5444317Z 2025-03-04T21:04:05.5444604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5444758Z out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:04:05.5444826Z 2025-03-04T21:04:05.5445102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5445589Z x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5445665Z 2025-03-04T21:04:05.5445932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5447706Z x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5447796Z 2025-03-04T21:04:05.5448077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5448227Z out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T21:04:05.5448292Z 2025-03-04T21:04:05.5448546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5449022Z x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5449094Z 2025-03-04T21:04:05.5449352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5451087Z x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5451180Z 2025-03-04T21:04:05.5451459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5451625Z out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:04:05.5451691Z 2025-03-04T21:04:05.5451945Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5452433Z x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5452504Z 2025-03-04T21:04:05.5452777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5454612Z x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5454693Z 2025-03-04T21:04:05.5454976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5455138Z x_105 += out_59; out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T21:04:05.5455205Z 2025-03-04T21:04:05.5455503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5455653Z out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:04:05.5455730Z 2025-03-04T21:04:05.5456004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5456506Z x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5456595Z 2025-03-04T21:04:05.5456861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5458679Z x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5458758Z 2025-03-04T21:04:05.5459045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5459193Z out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T21:04:05.5459260Z 2025-03-04T21:04:05.5459536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5460027Z x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5460102Z 2025-03-04T21:04:05.5460365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5462175Z x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5462250Z 2025-03-04T21:04:05.5462536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5462680Z out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T21:04:05.5462747Z 2025-03-04T21:04:05.5463023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5463526Z x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5463601Z 2025-03-04T21:04:05.5463865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5465701Z x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5465778Z 2025-03-04T21:04:05.5466058Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5466235Z x_111 += out_63; out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T21:04:05.5466302Z 2025-03-04T21:04:05.5466595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5466739Z out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T21:04:05.5466813Z 2025-03-04T21:04:05.5467064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5467555Z x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5467621Z 2025-03-04T21:04:05.5467894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5469700Z x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5469787Z 2025-03-04T21:04:05.5470084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5470222Z out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T21:04:05.5470295Z 2025-03-04T21:04:05.5470552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5471030Z x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5471103Z 2025-03-04T21:04:05.5471362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5473960Z x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5474057Z 2025-03-04T21:04:05.5474345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5474490Z out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T21:04:05.5474556Z 2025-03-04T21:04:05.5474823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5475306Z x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5475382Z 2025-03-04T21:04:05.5475648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5477496Z x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5477591Z 2025-03-04T21:04:05.5477872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5478032Z x_117 += out_67; out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T21:04:05.5478098Z 2025-03-04T21:04:05.5478388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5478532Z out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T21:04:05.5478610Z 2025-03-04T21:04:05.5478861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5479372Z x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5479441Z 2025-03-04T21:04:05.5479716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5481532Z x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5481618Z 2025-03-04T21:04:05.5481921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5482066Z out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T21:04:05.5482142Z 2025-03-04T21:04:05.5482397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5482895Z x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5482959Z 2025-03-04T21:04:05.5483233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5485027Z x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5485120Z 2025-03-04T21:04:05.5485417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5485556Z out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T21:04:05.5485629Z 2025-03-04T21:04:05.5485901Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5486406Z x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5486479Z 2025-03-04T21:04:05.5486746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5488748Z x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5488834Z 2025-03-04T21:04:05.5489120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5489284Z x_123 += out_71; out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T21:04:05.5489351Z 2025-03-04T21:04:05.5489644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5489788Z out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T21:04:05.5489863Z 2025-03-04T21:04:05.5490117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5490656Z x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5490747Z 2025-03-04T21:04:05.5491020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5492844Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5492914Z 2025-03-04T21:04:05.5493211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5493349Z out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T21:04:05.5493423Z 2025-03-04T21:04:05.5493676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5494283Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5494364Z 2025-03-04T21:04:05.5494670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5496546Z x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5496614Z 2025-03-04T21:04:05.5496914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5497052Z out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T21:04:05.5497125Z 2025-03-04T21:04:05.5497403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5497921Z x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5497996Z 2025-03-04T21:04:05.5498260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5500074Z x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5500143Z 2025-03-04T21:04:05.5500430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5500609Z x_129 += out_75; out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T21:04:05.5500678Z 2025-03-04T21:04:05.5500971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5501121Z out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T21:04:05.5501192Z 2025-03-04T21:04:05.5501444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5501937Z x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5502004Z 2025-03-04T21:04:05.5502278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5504106Z x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5504199Z 2025-03-04T21:04:05.5504492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5504628Z out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T21:04:05.5504702Z 2025-03-04T21:04:05.5504955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5505444Z x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5505512Z 2025-03-04T21:04:05.5505786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5507589Z x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5507674Z 2025-03-04T21:04:05.5507967Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5508103Z out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T21:04:05.5508174Z 2025-03-04T21:04:05.5508424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5508921Z x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5508990Z 2025-03-04T21:04:05.5509264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5511072Z x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5511159Z 2025-03-04T21:04:05.5511451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5511604Z x_135 += out_79; out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T21:04:05.5511683Z 2025-03-04T21:04:05.5511970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5512126Z out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T21:04:05.5512194Z 2025-03-04T21:04:05.5512455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5512970Z x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5513036Z 2025-03-04T21:04:05.5513309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5515087Z x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5515181Z 2025-03-04T21:04:05.5515474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5515614Z out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T21:04:05.5515688Z 2025-03-04T21:04:05.5515948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5516425Z x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5516491Z 2025-03-04T21:04:05.5516756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5518555Z x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5518647Z 2025-03-04T21:04:05.5518943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5519084Z out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T21:04:05.5519154Z 2025-03-04T21:04:05.5519420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5519915Z x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5519982Z 2025-03-04T21:04:05.5520257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5522051Z x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5522123Z 2025-03-04T21:04:05.5522414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5522566Z x_141 += out_83; out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T21:04:05.5522640Z 2025-03-04T21:04:05.5522923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5523075Z out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T21:04:05.5523141Z 2025-03-04T21:04:05.5523401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5523906Z x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5523994Z 2025-03-04T21:04:05.5524261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5526066Z x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5526145Z 2025-03-04T21:04:05.5526437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5526582Z out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T21:04:05.5526646Z 2025-03-04T21:04:05.5526910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5527417Z x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5527484Z 2025-03-04T21:04:05.5527753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5529519Z x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5529593Z 2025-03-04T21:04:05.5529882Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5530018Z out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T21:04:05.5530090Z 2025-03-04T21:04:05.5530355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5530860Z x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5530940Z 2025-03-04T21:04:05.5531205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5532964Z x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5533040Z 2025-03-04T21:04:05.5533319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5533466Z x_147 += out_87; out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T21:04:05.5533561Z 2025-03-04T21:04:05.5533850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5534003Z out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T21:04:05.5534069Z 2025-03-04T21:04:05.5534402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5534920Z x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5535001Z 2025-03-04T21:04:05.5535281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5537117Z x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5537215Z 2025-03-04T21:04:05.5537498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5537645Z out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T21:04:05.5537712Z 2025-03-04T21:04:05.5537984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5538463Z x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5538541Z 2025-03-04T21:04:05.5538809Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5540638Z x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5540734Z 2025-03-04T21:04:05.5541028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5541171Z out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T21:04:05.5541236Z 2025-03-04T21:04:05.5541489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5541974Z x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5542041Z 2025-03-04T21:04:05.5542308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5544062Z x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5544153Z 2025-03-04T21:04:05.5544434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5544579Z x_153 += out_91; out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T21:04:05.5544649Z 2025-03-04T21:04:05.5544924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5545070Z out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T21:04:05.5545135Z 2025-03-04T21:04:05.5545387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5545871Z x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5545943Z 2025-03-04T21:04:05.5546200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5547972Z x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5548063Z 2025-03-04T21:04:05.5548339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5548484Z out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T21:04:05.5548550Z 2025-03-04T21:04:05.5548798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5549275Z x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5549347Z 2025-03-04T21:04:05.5549605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5551364Z x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5551454Z 2025-03-04T21:04:05.5551735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5551878Z out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T21:04:05.5551943Z 2025-03-04T21:04:05.5552209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5552683Z x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5552754Z 2025-03-04T21:04:05.5553020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5554768Z x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5554842Z 2025-03-04T21:04:05.5555115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5555270Z x_159 += out_95; out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T21:04:05.5555334Z 2025-03-04T21:04:05.5555618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5555764Z out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T21:04:05.5555828Z 2025-03-04T21:04:05.5556080Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5556565Z x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5556652Z 2025-03-04T21:04:05.5556910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5558669Z x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5558747Z 2025-03-04T21:04:05.5559028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5559176Z out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T21:04:05.5559240Z 2025-03-04T21:04:05.5559494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5559989Z x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5560060Z 2025-03-04T21:04:05.5560318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5562056Z x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5562127Z 2025-03-04T21:04:05.5562407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5562551Z out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T21:04:05.5562615Z 2025-03-04T21:04:05.5562887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5563374Z x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5563468Z 2025-03-04T21:04:05.5563727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5565481Z x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5565558Z 2025-03-04T21:04:05.5565834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5565994Z x_165 += out_99; out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T21:04:05.5566076Z 2025-03-04T21:04:05.5566363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5566512Z out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T21:04:05.5566583Z 2025-03-04T21:04:05.5566829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5567314Z x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5567388Z 2025-03-04T21:04:05.5567648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5569431Z x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5569522Z 2025-03-04T21:04:05.5569806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5569950Z out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T21:04:05.5570015Z 2025-03-04T21:04:05.5570270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5570748Z x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5570822Z 2025-03-04T21:04:05.5571083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5572872Z x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5572963Z 2025-03-04T21:04:05.5573254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5573401Z out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T21:04:05.5573466Z 2025-03-04T21:04:05.5573726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5574276Z x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5574359Z 2025-03-04T21:04:05.5574629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5576489Z x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5576578Z 2025-03-04T21:04:05.5576860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5577033Z x_171 += out_103; out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T21:04:05.5577101Z 2025-03-04T21:04:05.5577390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5577542Z out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T21:04:05.5577617Z 2025-03-04T21:04:05.5577873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5578385Z x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5578453Z 2025-03-04T21:04:05.5578756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5580567Z x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5580699Z 2025-03-04T21:04:05.5581036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5581179Z out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T21:04:05.5581263Z 2025-03-04T21:04:05.5581570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5582093Z x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5582179Z 2025-03-04T21:04:05.5582463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5584294Z x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5584395Z 2025-03-04T21:04:05.5584694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5584841Z out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T21:04:05.5584908Z 2025-03-04T21:04:05.5585194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5585714Z x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5585787Z 2025-03-04T21:04:05.5586060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5587876Z x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5587952Z 2025-03-04T21:04:05.5588423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5588602Z x_177 += out_107; out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T21:04:05.5588669Z 2025-03-04T21:04:05.5588972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5589126Z out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T21:04:05.5589199Z 2025-03-04T21:04:05.5589446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5589972Z x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5590061Z 2025-03-04T21:04:05.5590330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5592124Z x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5592191Z 2025-03-04T21:04:05.5592491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5592631Z out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T21:04:05.5592708Z 2025-03-04T21:04:05.5592962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5593480Z x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5593548Z 2025-03-04T21:04:05.5593820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5595632Z x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5595700Z 2025-03-04T21:04:05.5595995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5596135Z out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T21:04:05.5596211Z 2025-03-04T21:04:05.5596477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5596978Z x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5597069Z 2025-03-04T21:04:05.5597336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5599165Z x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5599243Z 2025-03-04T21:04:05.5599525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5599696Z x_183 += out_111; out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T21:04:05.5599777Z 2025-03-04T21:04:05.5600072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5600222Z out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T21:04:05.5600295Z 2025-03-04T21:04:05.5600544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5601035Z x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5601101Z 2025-03-04T21:04:05.5601386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5603162Z x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5603288Z 2025-03-04T21:04:05.5603574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5603709Z out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T21:04:05.5603781Z 2025-03-04T21:04:05.5604026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5604511Z x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5604577Z 2025-03-04T21:04:05.5604843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5606601Z x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5606680Z 2025-03-04T21:04:05.5606966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5607103Z out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T21:04:05.5607174Z 2025-03-04T21:04:05.5607420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5607912Z x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5607985Z 2025-03-04T21:04:05.5608245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5610042Z x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5610127Z 2025-03-04T21:04:05.5610420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5610590Z x_189 += out_115; out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T21:04:05.5610658Z 2025-03-04T21:04:05.5610953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5611103Z out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T21:04:05.5611179Z 2025-03-04T21:04:05.5611432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5611938Z x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5612004Z 2025-03-04T21:04:05.5612277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5614033Z x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5616722Z 2025-03-04T21:04:05.5617022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5617170Z out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191); x_191 = None 2025-03-04T21:04:05.5617244Z 2025-03-04T21:04:05.5617493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5617990Z x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5618057Z 2025-03-04T21:04:05.5618331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5620201Z x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5620276Z 2025-03-04T21:04:05.5620566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5620701Z out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193); x_193 = None 2025-03-04T21:04:05.5620772Z 2025-03-04T21:04:05.5621035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5621523Z x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5621586Z 2025-03-04T21:04:05.5621852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5623615Z x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5623756Z 2025-03-04T21:04:05.5624016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5624499Z x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:04:05.5624572Z 2025-03-04T21:04:05.5624831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5626661Z x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5626736Z 2025-03-04T21:04:05.5627009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5627172Z x_195 += x_197; out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195; x_195 = x_197 = None 2025-03-04T21:04:05.5627237Z 2025-03-04T21:04:05.5627537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5627680Z out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122); out_122 = None 2025-03-04T21:04:05.5627751Z 2025-03-04T21:04:05.5627997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5628475Z x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5628555Z 2025-03-04T21:04:05.5628825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5630572Z x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5630665Z 2025-03-04T21:04:05.5630951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5631084Z out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199); x_199 = None 2025-03-04T21:04:05.5631157Z 2025-03-04T21:04:05.5631401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5631896Z x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5631973Z 2025-03-04T21:04:05.5632231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5633972Z x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5634062Z 2025-03-04T21:04:05.5634342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5634484Z out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201); x_201 = None 2025-03-04T21:04:05.5634550Z 2025-03-04T21:04:05.5634804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5635288Z x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5635383Z 2025-03-04T21:04:05.5635647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5637410Z x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5637503Z 2025-03-04T21:04:05.5637780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5637943Z x_203 += out_123; out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203; x_203 = out_123 = None 2025-03-04T21:04:05.5638009Z 2025-03-04T21:04:05.5638295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5638454Z out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126); out_126 = None 2025-03-04T21:04:05.5638527Z 2025-03-04T21:04:05.5638775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5639250Z x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.5639315Z 2025-03-04T21:04:05.5639592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5641380Z x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5641450Z 2025-03-04T21:04:05.5641740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5641890Z out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205); x_205 = None 2025-03-04T21:04:05.5641963Z 2025-03-04T21:04:05.5642207Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5642697Z x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.5642764Z 2025-03-04T21:04:05.5643037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5644830Z x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5644900Z 2025-03-04T21:04:05.5645225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5645363Z out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207); x_207 = None 2025-03-04T21:04:05.5645437Z 2025-03-04T21:04:05.5645690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5646188Z x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.5646262Z 2025-03-04T21:04:05.5646529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.5648328Z x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.5648420Z 2025-03-04T21:04:05.5648703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.5648877Z x_209 += out_127; out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209; x_209 = out_127 = None 2025-03-04T21:04:05.5648944Z 2025-03-04T21:04:05.5649240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.5649389Z out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130); out_130 = None 2025-03-04T21:04:05.5649463Z 2025-03-04T21:04:05.5649717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5650328Z x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T21:04:05.5650396Z 2025-03-04T21:04:05.5650655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5651205Z x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T21:04:05.5651280Z 2025-03-04T21:04:05.5651712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:04:05.5651994Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest'); x_210 = None 2025-03-04T21:04:05.5652061Z 2025-03-04T21:04:05.5652321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5652898Z x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T21:04:05.5652967Z 2025-03-04T21:04:05.5653324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:04:05.5653538Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features; x_212 = top_down_features = None 2025-03-04T21:04:05.5653610Z 2025-03-04T21:04:05.5653862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5654530Z x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T21:04:05.5654626Z 2025-03-04T21:04:05.5655058Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:04:05.5655402Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T21:04:05.5655477Z 2025-03-04T21:04:05.5655727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5656314Z x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T21:04:05.5656410Z 2025-03-04T21:04:05.5656758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:04:05.5656978Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1; x_214 = top_down_features_1 = None 2025-03-04T21:04:05.5657048Z 2025-03-04T21:04:05.5657308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5657882Z x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T21:04:05.5657960Z 2025-03-04T21:04:05.5658379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:04:05.5658719Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T21:04:05.5658787Z 2025-03-04T21:04:05.5659050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5659628Z x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T21:04:05.5659704Z 2025-03-04T21:04:05.5660061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:04:05.5660295Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2; x_216 = top_down_features_2 = None 2025-03-04T21:04:05.5660370Z 2025-03-04T21:04:05.5660625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5661246Z x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T21:04:05.5661328Z 2025-03-04T21:04:05.5661698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T21:04:05.5661920Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T21:04:05.5661993Z 2025-03-04T21:04:05.5662444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.5662609Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:04:05.5662693Z 2025-03-04T21:04:05.5662998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.5663140Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:04:05.5663227Z 2025-03-04T21:04:05.5663651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.5663806Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:04:05.5663871Z 2025-03-04T21:04:05.5664166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.5664306Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:04:05.5664379Z 2025-03-04T21:04:05.5664760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:05.5664946Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:04:05.5665053Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:04:05.5665176Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:04:05.5665248Z 2025-03-04T21:04:05.5665573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:05.5665711Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:04:05.5665777Z 2025-03-04T21:04:05.5666108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:05.5666235Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:04:05.5666307Z 2025-03-04T21:04:05.5666697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:05.5666920Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:04:05.5666984Z 2025-03-04T21:04:05.5667404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:05.5667530Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:04:05.5667977Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:04:05.5668101Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:04:05.5668226Z x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:04:05.5668290Z 2025-03-04T21:04:05.5668717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.5668864Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:04:05.5668948Z 2025-03-04T21:04:05.5669237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.5669384Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:04:05.5669449Z 2025-03-04T21:04:05.5669874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.5670015Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:04:05.5670086Z 2025-03-04T21:04:05.5670368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.5670513Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:04:05.5670578Z 2025-03-04T21:04:05.5670961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:05.5671165Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:04:05.5671271Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:04:05.5671403Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:04:05.5671467Z 2025-03-04T21:04:05.5671794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:05.5671921Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:04:05.5671994Z 2025-03-04T21:04:05.5672314Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:05.5672445Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:04:05.5672510Z 2025-03-04T21:04:05.5672905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:05.5673116Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:04:05.5673187Z 2025-03-04T21:04:05.5673587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:05.5673746Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:04:05.5674164Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:04:05.5674298Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:04:05.5674415Z x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:04:05.5674486Z 2025-03-04T21:04:05.5674908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.5675078Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:04:05.5675144Z 2025-03-04T21:04:05.5675437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.5675576Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:04:05.5675652Z 2025-03-04T21:04:05.5676065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.5676215Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:04:05.5676278Z 2025-03-04T21:04:05.5676570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.5676704Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:04:05.5676777Z 2025-03-04T21:04:05.5677158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:05.5677360Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:04:05.5677468Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:04:05.5677587Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:04:05.5677652Z 2025-03-04T21:04:05.5677982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:05.5678115Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:04:05.5678181Z 2025-03-04T21:04:05.5678509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:05.5678628Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:04:05.5678699Z 2025-03-04T21:04:05.5679082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:05.5679300Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:04:05.5679364Z 2025-03-04T21:04:05.5679772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:05.5679919Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:04:05.5680334Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:04:05.5680456Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:04:05.5680578Z x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T21:04:05.5680643Z 2025-03-04T21:04:05.5681068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.5681225Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:04:05.5681299Z 2025-03-04T21:04:05.5681589Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.5681731Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:04:05.5681796Z 2025-03-04T21:04:05.5682223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.5682362Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:04:05.5682435Z 2025-03-04T21:04:05.5682723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.5682864Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:04:05.5682949Z 2025-03-04T21:04:05.5683324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:05.5683520Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:04:05.5683621Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:04:05.5683747Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:04:05.5683811Z 2025-03-04T21:04:05.5684134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:05.5684258Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:04:05.5684331Z 2025-03-04T21:04:05.5684650Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:05.5684794Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:04:05.5684860Z 2025-03-04T21:04:05.5685240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:05.5685453Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:04:05.5685523Z 2025-03-04T21:04:05.5685924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:05.5686071Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:04:05.5686477Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:04:05.5686606Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:04:05.5686720Z x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T21:04:05.5686790Z 2025-03-04T21:04:05.5687200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.5687373Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:04:05.5687438Z 2025-03-04T21:04:05.5687734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.5687869Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:04:05.5687942Z 2025-03-04T21:04:05.5688530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.5688691Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:04:05.5688757Z 2025-03-04T21:04:05.5689052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.5689228Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:04:05.5689306Z 2025-03-04T21:04:05.5689686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:05.5689890Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:04:05.5690001Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:04:05.5690124Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:04:05.5690193Z 2025-03-04T21:04:05.5690531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:05.5690669Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:04:05.5690737Z 2025-03-04T21:04:05.5691070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:05.5691212Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:04:05.5691286Z 2025-03-04T21:04:05.5691671Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:05.5691892Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:04:05.5691960Z 2025-03-04T21:04:05.5692385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:05.5692538Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:04:05.5692964Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:04:05.5693089Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:04:05.5693212Z x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T21:04:05.5693277Z 2025-03-04T21:04:05.5693587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:04:05.5693750Z tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32); x_218 = None 2025-03-04T21:04:05.5693900Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32); x_219 = None 2025-03-04T21:04:05.5694025Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32); x_220 = None 2025-03-04T21:04:05.5694207Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32); x_221 = None 2025-03-04T21:04:05.5694341Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32); x_222 = None 2025-03-04T21:04:05.5694418Z 2025-03-04T21:04:05.5694685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5695223Z x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_217 = None 2025-03-04T21:04:05.5695294Z 2025-03-04T21:04:05.5695606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:05.5695825Z x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False); x_223 = None 2025-03-04T21:04:05.5695899Z 2025-03-04T21:04:05.5696286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:05.5696815Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:05.5696892Z 2025-03-04T21:04:05.5697256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:05.5697801Z x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_224 = None 2025-03-04T21:04:05.5697870Z 2025-03-04T21:04:05.5698133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5698616Z x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_215 = None 2025-03-04T21:04:05.5698713Z 2025-03-04T21:04:05.5698991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:05.5699199Z x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False); x_225 = None 2025-03-04T21:04:05.5699264Z 2025-03-04T21:04:05.5699650Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:05.5700184Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:05.5700267Z 2025-03-04T21:04:05.5700628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:05.5701131Z x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_226 = None 2025-03-04T21:04:05.5701204Z 2025-03-04T21:04:05.5701455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5701924Z x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_213 = None 2025-03-04T21:04:05.5702006Z 2025-03-04T21:04:05.5702283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:05.5702469Z x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False); x_227 = None 2025-03-04T21:04:05.5702543Z 2025-03-04T21:04:05.5702910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:05.5703401Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:05.5703468Z 2025-03-04T21:04:05.5703820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:05.5704324Z x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_228 = None 2025-03-04T21:04:05.5704390Z 2025-03-04T21:04:05.5704646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5705102Z x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_211 = None 2025-03-04T21:04:05.5705190Z 2025-03-04T21:04:05.5705458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:05.5705646Z x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False); x_229 = None 2025-03-04T21:04:05.5705712Z 2025-03-04T21:04:05.5706082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:05.5706561Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:05.5706659Z 2025-03-04T21:04:05.5707004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:05.5707497Z x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_230 = None 2025-03-04T21:04:05.5707568Z 2025-03-04T21:04:05.5707814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.5708567Z x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:04:05.5708637Z 2025-03-04T21:04:05.5708911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:05.5709087Z x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False); x_231 = None 2025-03-04T21:04:05.5709159Z 2025-03-04T21:04:05.5709520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:05.5710378Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:04:05.5710453Z 2025-03-04T21:04:05.5710806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:05.5711609Z x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:04:05.5711691Z 2025-03-04T21:04:05.5712040Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:04:05.5712205Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:04:05.5712356Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:04:05.5712520Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:04:05.5712673Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:04:05.5712843Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:04:05.5712991Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:04:05.5713136Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:04:05.5713278Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:04:05.5713421Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:04:05.5713562Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:04:05.5713630Z 2025-03-04T21:04:05.5714057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:04:05.5714244Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304); x_233 = None 2025-03-04T21:04:05.5714446Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:04:05.5714636Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:04:05.5714801Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152); x_234 = None 2025-03-04T21:04:05.5714985Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:04:05.5715157Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:04:05.5715316Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76); x_235 = None 2025-03-04T21:04:05.5715482Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:04:05.5715656Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:04:05.5715817Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38); x_236 = None 2025-03-04T21:04:05.5715985Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:04:05.5716148Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:04:05.5716291Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19); x_237 = None 2025-03-04T21:04:05.5716446Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:04:05.5716638Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:04:05.5716708Z 2025-03-04T21:04:05.5717125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:05.5717326Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:04:05.5717399Z 2025-03-04T21:04:05.5717832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:05.5717994Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:04:05.5718159Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:04:05.5718309Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:04:05.5718374Z 2025-03-04T21:04:05.5718756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:05.5718935Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:04:05.5718999Z 2025-03-04T21:04:05.5719316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:05.5719457Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:04:05.5719529Z 2025-03-04T21:04:05.5719838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:05.5719994Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:04:05.5720124Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:05.5720281Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:04:05.5720345Z 2025-03-04T21:04:05.5720669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:05.5720797Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:04:05.5720924Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:04:05.5721076Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:04:05.5721150Z 2025-03-04T21:04:05.5721456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:05.5721586Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:05.5721743Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:04:05.5721881Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:04:05.5721946Z 2025-03-04T21:04:05.5722256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:05.5722401Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:04:05.5722502Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:04:05.5722649Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:04:05.5722720Z 2025-03-04T21:04:05.5723059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:05.5723224Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:05.5723340Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:04:05.5723411Z 2025-03-04T21:04:05.5723710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:05.5723874Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:05.5724025Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:04:05.5724099Z 2025-03-04T21:04:05.5724394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:05.5724554Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:05.5724667Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:04:05.5724739Z 2025-03-04T21:04:05.5725043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:05.5725233Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:04:05.5725353Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:04:05.5725419Z 2025-03-04T21:04:05.5725781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:05.5725927Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:04:05.5726001Z 2025-03-04T21:04:05.5726330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:05.5726472Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:04:05.5726537Z 2025-03-04T21:04:05.5726887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:05.5727028Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:04:05.5727163Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:04:05.5727317Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:04:05.5727461Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:04:05.5727541Z 2025-03-04T21:04:05.5727889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:05.5728030Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:04:05.5728161Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:04:05.5728310Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:04:05.5728453Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:04:05.5728543Z 2025-03-04T21:04:05.5728878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:05.5728999Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:04:05.5729168Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:04:05.5729300Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:04:05.5729371Z 2025-03-04T21:04:05.5729698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:05.5729844Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:04:05.5730010Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:04:05.5730152Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:04:05.5730217Z 2025-03-04T21:04:05.5730530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:05.5730628Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:04:05.5730756Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:04:05.5730820Z 2025-03-04T21:04:05.5731133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:05.5731232Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:04:05.5731362Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:04:05.5731427Z 2025-03-04T21:04:05.5731755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:05.5731876Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:04:05.5732037Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:04:05.5732103Z 2025-03-04T21:04:05.5732415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:05.5732531Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:04:05.5732676Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:04:05.5732741Z 2025-03-04T21:04:05.5733091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:05.5733276Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:04:05.5733349Z 2025-03-04T21:04:05.5733703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:05.5733880Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:04:05.5733953Z 2025-03-04T21:04:05.5734413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:05.5734608Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:04:05.5734692Z 2025-03-04T21:04:05.5735110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:05.5735330Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:04:05.5735405Z 2025-03-04T21:04:05.5735845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:05.5736016Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:04:05.5736189Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:04:05.5736345Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:04:05.5736411Z 2025-03-04T21:04:05.5736803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:05.5736978Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:04:05.5737051Z 2025-03-04T21:04:05.5737364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:05.5737522Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:04:05.5737590Z 2025-03-04T21:04:05.5737907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:05.5738068Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:04:05.5738206Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:04:05.5738358Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:04:05.5738432Z 2025-03-04T21:04:05.5738748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:05.5738883Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:04:05.5739008Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:04:05.5739168Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:04:05.5739234Z 2025-03-04T21:04:05.5739551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:05.5739677Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:04:05.5739791Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:04:05.5739926Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:04:05.5739999Z 2025-03-04T21:04:05.5740308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:05.5740466Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:04:05.5740563Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:04:05.5740722Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:04:05.5740788Z 2025-03-04T21:04:05.5741103Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:05.5741270Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:05.5741388Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:04:05.5741460Z 2025-03-04T21:04:05.5741761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:05.5741921Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:05.5742061Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:04:05.5742138Z 2025-03-04T21:04:05.5742441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:05.5742601Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:05.5742718Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:04:05.5742790Z 2025-03-04T21:04:05.5743095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:05.5743293Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:04:05.5743409Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:04:05.5743482Z 2025-03-04T21:04:05.5743838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:05.5743993Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:04:05.5744060Z 2025-03-04T21:04:05.5744401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:05.5744539Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:04:05.5744614Z 2025-03-04T21:04:05.5744958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:05.5745108Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:04:05.5745237Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:04:05.5745404Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:04:05.5745566Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:04:05.5745640Z 2025-03-04T21:04:05.5745990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:05.5746137Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:04:05.5746262Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:04:05.5746429Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:04:05.5746593Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:04:05.5746659Z 2025-03-04T21:04:05.5746995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:05.5747114Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:04:05.5747284Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:04:05.5747421Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:04:05.5747494Z 2025-03-04T21:04:05.5747823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:05.5747963Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:04:05.5748146Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:04:05.5748285Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:04:05.5748350Z 2025-03-04T21:04:05.5748661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:05.5748761Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:04:05.5748886Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:04:05.5748951Z 2025-03-04T21:04:05.5749261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:05.5749359Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:04:05.5749501Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:04:05.5749568Z 2025-03-04T21:04:05.5749882Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:05.5750004Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:04:05.5750148Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:04:05.5750214Z 2025-03-04T21:04:05.5750525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:05.5750641Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:04:05.5750784Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:04:05.5750851Z 2025-03-04T21:04:05.5751206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:05.5751423Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:04:05.5751494Z 2025-03-04T21:04:05.5751829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:05.5751997Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:04:05.5752060Z 2025-03-04T21:04:05.5752463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:05.5752659Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:04:05.5752732Z 2025-03-04T21:04:05.5753134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:05.5753352Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:04:05.5753417Z 2025-03-04T21:04:05.5753868Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:05.5754038Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:04:05.5754201Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:04:05.5754348Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:04:05.5754413Z 2025-03-04T21:04:05.5754791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:05.5754960Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:04:05.5755037Z 2025-03-04T21:04:05.5755347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:05.5755500Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:04:05.5755567Z 2025-03-04T21:04:05.5755902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:05.5756034Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:04:05.5756170Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:04:05.5756322Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:04:05.5756399Z 2025-03-04T21:04:05.5756714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:05.5756848Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:04:05.5756978Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:04:05.5757141Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:04:05.5757208Z 2025-03-04T21:04:05.5757528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:05.5757671Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:04:05.5757788Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:04:05.5757923Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:04:05.5757996Z 2025-03-04T21:04:05.5758318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:05.5758481Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:04:05.5758592Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:04:05.5758731Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:04:05.5758797Z 2025-03-04T21:04:05.5759112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:05.5759267Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:05.5759392Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:04:05.5759457Z 2025-03-04T21:04:05.5759763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:05.5759937Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:05.5760075Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:04:05.5760141Z 2025-03-04T21:04:05.5760450Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:05.5760612Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:05.5760729Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:04:05.5760802Z 2025-03-04T21:04:05.5761106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:05.5761299Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:04:05.5761416Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:04:05.5761497Z 2025-03-04T21:04:05.5761852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:05.5762005Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:04:05.5762077Z 2025-03-04T21:04:05.5762421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:05.5762559Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:04:05.5762629Z 2025-03-04T21:04:05.5762977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:05.5763133Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:04:05.5763270Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:04:05.5763433Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:04:05.5763598Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:04:05.5763672Z 2025-03-04T21:04:05.5764021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:05.5764176Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:04:05.5764303Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:04:05.5764481Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:04:05.5764629Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:04:05.5764702Z 2025-03-04T21:04:05.5765033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:05.5765160Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:04:05.5765321Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:04:05.5765465Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:04:05.5765531Z 2025-03-04T21:04:05.5765873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:05.5766009Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:04:05.5766185Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:04:05.5766343Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:04:05.5766409Z 2025-03-04T21:04:05.5766727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:05.5766826Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:04:05.5766953Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:04:05.5767019Z 2025-03-04T21:04:05.5767335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:05.5767433Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:04:05.5767583Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:04:05.5767657Z 2025-03-04T21:04:05.5767973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:05.5768090Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:04:05.5768233Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:04:05.5768299Z 2025-03-04T21:04:05.5768609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:05.5768725Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:04:05.5768867Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:04:05.5768934Z 2025-03-04T21:04:05.5769286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:05.5769495Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:04:05.5769570Z 2025-03-04T21:04:05.5769907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:05.5770081Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:04:05.5770149Z 2025-03-04T21:04:05.5770540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:05.5770733Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:04:05.5770813Z 2025-03-04T21:04:05.5771218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:05.5771433Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:04:05.5771499Z 2025-03-04T21:04:05.5771937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:05.5772105Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:04:05.5772266Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:04:05.5772404Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:04:05.5772479Z 2025-03-04T21:04:05.5772847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:05.5773021Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:04:05.5773093Z 2025-03-04T21:04:05.5773403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:05.5773559Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:04:05.5773626Z 2025-03-04T21:04:05.5773973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:05.5774125Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:04:05.5774355Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:04:05.5774528Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:04:05.5774619Z 2025-03-04T21:04:05.5774958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:05.5775102Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:04:05.5775233Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:04:05.5775402Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:04:05.5775472Z 2025-03-04T21:04:05.5775839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:05.5775968Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:04:05.5776072Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:04:05.5776205Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:04:05.5776280Z 2025-03-04T21:04:05.5776592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:05.5776767Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:04:05.5776864Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:04:05.5777006Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:04:05.5777071Z 2025-03-04T21:04:05.5777386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:05.5777542Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:05.5777666Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:04:05.5777733Z 2025-03-04T21:04:05.5778044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:05.5778217Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:05.5778341Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:04:05.5778408Z 2025-03-04T21:04:05.5778718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:05.5778870Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:05.5778990Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:04:05.5779056Z 2025-03-04T21:04:05.5779364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:05.5779551Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:04:05.5779670Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:04:05.5779751Z 2025-03-04T21:04:05.5780096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:05.5780248Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:04:05.5780315Z 2025-03-04T21:04:05.5780654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:05.5780792Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:04:05.5780865Z 2025-03-04T21:04:05.5781212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:05.5781359Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:04:05.5781483Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:04:05.5781661Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:04:05.5781805Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:04:05.5781879Z 2025-03-04T21:04:05.5782233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:05.5782378Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:04:05.5782503Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:04:05.5782680Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:04:05.5782820Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:04:05.5782892Z 2025-03-04T21:04:05.5783223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:05.5783346Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:04:05.5783508Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:04:05.5783650Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:04:05.5783731Z 2025-03-04T21:04:05.5784080Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:05.5784199Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:04:05.5784375Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:04:05.5784509Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:04:05.5784583Z 2025-03-04T21:04:05.5784893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:05.5784999Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:04:05.5785119Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:04:05.5785193Z 2025-03-04T21:04:05.5785502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:05.5785624Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:04:05.5785744Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:04:05.5785819Z 2025-03-04T21:04:05.5786126Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:05.5786251Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:04:05.5786385Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:04:05.5786457Z 2025-03-04T21:04:05.5786764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:05.5786890Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:04:05.5787021Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:04:05.5787093Z 2025-03-04T21:04:05.5787459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:05.5787663Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:04:05.5787729Z 2025-03-04T21:04:05.5788213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:05.5788396Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:04:05.5788499Z 2025-03-04T21:04:05.5788898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:05.5789072Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:04:05.5789146Z 2025-03-04T21:04:05.5789553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:05.5789774Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:04:05.5789840Z 2025-03-04T21:04:05.5790290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:05.5790467Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:04:05.5790624Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:04:05.5790763Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:04:05.5790837Z 2025-03-04T21:04:05.5791211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:05.5791388Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:04:05.5791454Z 2025-03-04T21:04:05.5791778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:05.5791944Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:04:05.5792018Z 2025-03-04T21:04:05.5792337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:05.5792476Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:04:05.5792604Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:04:05.5792761Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:04:05.5792826Z 2025-03-04T21:04:05.5793154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:05.5793280Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:04:05.5793416Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:04:05.5793568Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:04:05.5793643Z 2025-03-04T21:04:05.5793980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:05.5794111Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:04:05.5794204Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:04:05.5794342Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:04:05.5794408Z 2025-03-04T21:04:05.5794727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:05.5794898Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:04:05.5794999Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:04:05.5795129Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:04:05.5795206Z 2025-03-04T21:04:05.5795519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:05.5795677Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:05.5795798Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:04:05.5795863Z 2025-03-04T21:04:05.5796163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:05.5796340Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:05.5796462Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:04:05.5796527Z 2025-03-04T21:04:05.5796837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:05.5796988Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:05.5797106Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:04:05.5797171Z 2025-03-04T21:04:05.5797480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:05.5797668Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:04:05.5797802Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:04:05.5797867Z 2025-03-04T21:04:05.5798213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:05.5798354Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:04:05.5798427Z 2025-03-04T21:04:05.5798759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:05.5798905Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:04:05.5798972Z 2025-03-04T21:04:05.5799323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:05.5799463Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:04:05.5799593Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:04:05.5799762Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:04:05.5799910Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:04:05.5799976Z 2025-03-04T21:04:05.5800332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:05.5800470Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:04:05.5800618Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:04:05.5800773Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:04:05.5800921Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:04:05.5800986Z 2025-03-04T21:04:05.5801326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:05.5801452Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:04:05.5801614Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:04:05.5801753Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:04:05.5801845Z 2025-03-04T21:04:05.5802176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:05.5802289Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:04:05.5802457Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:04:05.5802585Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:04:05.5802656Z 2025-03-04T21:04:05.5802958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:05.5803062Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:04:05.5803176Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:04:05.5803246Z 2025-03-04T21:04:05.5803548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:05.5803664Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:04:05.5803777Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:04:05.5803846Z 2025-03-04T21:04:05.5804144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:05.5804264Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:04:05.5804392Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:04:05.5804462Z 2025-03-04T21:04:05.5804758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:05.5804881Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:04:05.5805010Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:04:05.5805082Z 2025-03-04T21:04:05.5805432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:05.5805623Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:04:05.5805688Z 2025-03-04T21:04:05.5806018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:05.5806176Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:04:05.5806266Z 2025-03-04T21:04:05.5806637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:05.5806818Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:04:05.5806882Z 2025-03-04T21:04:05.5807359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:04:05.5807493Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:04:05.5807565Z 2025-03-04T21:04:05.5807859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.5808025Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:04:05.5808092Z 2025-03-04T21:04:05.5808531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:05.5808648Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:04:05.5808762Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:04:05.5808879Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:04:05.5808955Z 2025-03-04T21:04:05.5809412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:05.5809556Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:05.5809808Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:04:05.5809876Z 2025-03-04T21:04:05.5810339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:05.5810517Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:05.5810592Z 2025-03-04T21:04:05.5810895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.5811032Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:04:05.5811101Z 2025-03-04T21:04:05.5811551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:05.5811693Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:04:05.5811817Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:04:05.5811940Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:04:05.5812016Z 2025-03-04T21:04:05.5812488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:05.5812641Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:05.5812898Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:04:05.5812971Z 2025-03-04T21:04:05.5813428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:05.5813605Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:05.5813672Z 2025-03-04T21:04:05.5813982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.5814115Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:04:05.5814269Z 2025-03-04T21:04:05.5814764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:05.5814901Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:04:05.5815023Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:04:05.5815165Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:04:05.5815239Z 2025-03-04T21:04:05.5815740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:05.5815879Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:05.5816131Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:04:05.5816209Z 2025-03-04T21:04:05.5816735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:05.5816917Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:05.5816987Z 2025-03-04T21:04:05.5817302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.5817434Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:04:05.5817515Z 2025-03-04T21:04:05.5817967Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:05.5818098Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:04:05.5818207Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:04:05.5818354Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:04:05.5818423Z 2025-03-04T21:04:05.5818897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:05.5819036Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:05.5819284Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:04:05.5819381Z 2025-03-04T21:04:05.5819850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:05.5820018Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:05.5820092Z 2025-03-04T21:04:05.5820390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.5820526Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:04:05.5820594Z 2025-03-04T21:04:05.5821041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:05.5821181Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:04:05.5821298Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:04:05.5821418Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:04:05.5821495Z 2025-03-04T21:04:05.5821957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:05.5822134Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:04:05.5822379Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:04:05.5822447Z 2025-03-04T21:04:05.5822941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:05.5823114Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:05.5823192Z 2025-03-04T21:04:05.5823495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.5823628Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:04:05.5823697Z 2025-03-04T21:04:05.5823998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:05.5824389Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:04:05.5824466Z 2025-03-04T21:04:05.5824768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:05.5825251Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:04:05.5825317Z 2025-03-04T21:04:05.5825591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:05.5825789Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:04:05.5825879Z 2025-03-04T21:04:05.5826256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:04:05.5826407Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:04:05.5826472Z 2025-03-04T21:04:05.5826774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:04:05.5826920Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:04:05.5826992Z 2025-03-04T21:04:05.5827360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:04:05.5827517Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:04:05.5827582Z 2025-03-04T21:04:05.5828061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:04:05.5828209Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:04:05.5828328Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:04:05.5828488Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:04:05.5828619Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:04:05.5828691Z 2025-03-04T21:04:05.5829044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:04:05.5829184Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:04:05.5829250Z 2025-03-04T21:04:05.5829986Z 2025-03-04T21:04:05.5830093Z class GraphModule(torch.nn.Module): 2025-03-04T21:04:05.5951543Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: 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L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: 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L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: 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1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:04:05.5952365Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:04:05.5952706Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.5953104Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5953490Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5953865Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5954223Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5954598Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.5955014Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5955416Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5955821Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5956191Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5956542Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.5956943Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5957376Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5957763Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5958135Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5958497Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.5958914Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5959320Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5959697Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5960072Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5960440Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:04:05.5960870Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5961285Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5961675Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5962070Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5962429Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.5962842Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5963251Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5963643Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5964025Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5964370Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.5964780Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5965176Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5965578Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5965962Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5966304Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.5966707Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5967102Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5967499Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5967876Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5968217Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.5968641Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5969038Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5969421Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5969790Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5970158Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.5970565Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5970961Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5971345Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5971731Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5972083Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.5972479Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5972881Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5973269Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5973651Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5974010Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.5974513Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5975004Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5975463Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5975843Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5976194Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.5976594Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5977015Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5977392Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5977768Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5978108Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.5978538Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5978944Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5979322Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5979701Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5980073Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:04:05.5980510Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5980919Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5981315Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5981722Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5982064Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.5982474Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5982870Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5983947Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5984319Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5984672Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.5985090Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5985502Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5985896Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5986268Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5986624Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.5987026Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5987433Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5987839Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5988360Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5988715Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.5989159Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5989571Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5989953Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5990335Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5990713Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.5991110Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5991514Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5991892Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5992305Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5992649Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.5993058Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5993461Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5993840Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5994242Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5994589Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.5995002Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5995398Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5995803Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5996183Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5996525Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.5996948Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5997351Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5997736Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.5998113Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.5998459Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.5998881Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.5999275Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.5999660Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6000027Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6000379Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6000799Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6001194Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6001581Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6001985Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6002339Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6002739Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6003142Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6003546Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6003918Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6004264Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6004664Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6005080Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6005460Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6005842Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6006207Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:04:05.6006625Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6007066Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6007458Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6007851Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6008197Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6008623Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6009019Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6009398Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6009796Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6010144Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6010629Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6011054Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6011440Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6011838Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6012258Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6012740Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6013177Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6013645Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6014108Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6014564Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6015053Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6015549Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6016004Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6016450Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6016849Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6017301Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6017722Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6018121Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6018512Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6018892Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6019316Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6019738Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6020144Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6020544Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6020915Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6021348Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6021785Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6022184Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6022596Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6022958Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6023394Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6023819Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6024229Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6024619Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6024964Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6025373Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6025793Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6026174Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6026551Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6026896Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6027302Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6027708Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6028101Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6028492Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6028838Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6029273Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6029669Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6030057Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6030443Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6030802Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6031213Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6031608Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6031995Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6032382Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6032737Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6033147Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6033560Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6033955Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6034349Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6034709Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6035117Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6035575Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6035961Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6036344Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6036702Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6037131Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6037546Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6037932Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6038319Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6038690Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6039121Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6039531Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6039928Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6040326Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6040703Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6041108Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6041500Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6041885Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6042273Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6042613Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6043012Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6043462Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6043846Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6044213Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6044561Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6044961Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6045366Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6045749Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6046120Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6046475Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6046885Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6047311Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6047705Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6048081Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6048454Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6048864Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6049276Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6049667Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6050075Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6050436Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6050841Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6051248Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6051648Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6052046Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6052411Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6052824Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6053241Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6053635Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6054052Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6054455Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6054875Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6055314Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6055701Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6056091Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6056445Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6056895Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6057290Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6057684Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6058074Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6058449Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6058868Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6059271Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6059667Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6060054Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6060429Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6060854Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6061259Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6061657Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6062048Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6062412Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6062827Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6063261Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6063658Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6064038Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6064398Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6064808Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6065240Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6065628Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6066015Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6066372Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6066785Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6067212Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6067601Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6067988Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6068361Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6068781Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6069196Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6069583Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6069989Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6070344Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6070764Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6071169Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6071584Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6071971Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6072324Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6072737Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6073140Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6073549Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6073925Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6074285Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6074706Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6075125Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6075520Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6075901Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6076277Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6076699Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6077106Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6077502Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6077882Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6078267Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6078677Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6079087Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6079483Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6079865Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6080240Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6080652Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6081063Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6081469Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6081871Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6082233Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6082651Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6083069Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6083446Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6083819Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6084161Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6084585Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6084989Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6085363Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6085739Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6086084Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6086505Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6086902Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6087289Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6087689Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6088039Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6088557Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6088956Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6089384Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6089768Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6090126Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6090536Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6090964Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6091370Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6091750Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6092116Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6092532Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6092949Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6093372Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6093757Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6094111Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6094574Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6094980Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6095364Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6095752Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6096129Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6096541Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6096948Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6097340Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6097734Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6098078Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6098482Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6098882Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6099260Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6099649Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6099997Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6100423Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6100850Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6101229Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6101610Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6101967Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6102406Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6102810Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6103206Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6103600Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6103954Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6104385Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6104791Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6105194Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6105571Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6105936Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6106365Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6106780Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6107179Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6107582Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6107940Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6108348Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6108761Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6109186Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6109570Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6109928Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6110350Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6110802Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6111194Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6111579Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6111942Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6112357Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6112783Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6113173Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6113560Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6113933Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6114352Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6114765Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6115140Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6115535Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6115882Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6116289Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6116685Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6117085Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6117461Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6117803Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6118208Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6118602Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6119008Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6119374Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6119725Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6120131Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6120542Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6120924Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6121293Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6121644Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6122066Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6122471Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6122859Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6123228Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6123598Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6123998Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6124396Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6124779Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6125150Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6125515Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6125913Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6126310Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6126712Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6127089Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6127439Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6127838Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6128263Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6128648Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6129022Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6129365Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6129792Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6130199Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6130577Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6130954Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6131305Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6131729Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6132128Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6132513Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6132890Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6133254Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6133667Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6134067Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6134536Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6134932Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6135310Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6135725Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6136127Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6136537Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6136910Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6137265Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6137670Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6138075Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6138505Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6138886Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6139245Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6139680Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6140101Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6140497Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6140893Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6141278Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6141696Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6142108Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6142499Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6142906Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6143260Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6143676Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6144087Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6144473Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6144857Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6145248Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:04:05.6145677Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6146107Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6146534Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6146933Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6147284Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6147709Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6148149Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6148547Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6148926Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6149294Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6149740Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6150158Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6150549Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6150932Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6151295Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6151748Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6152164Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6152570Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6152969Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6153330Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6153735Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6154134Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6154531Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6154901Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6155247Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6155643Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6156052Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6156432Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6156827Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6157180Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6157583Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6157987Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6158377Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6158755Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6158982Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T21:04:05.6159222Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T21:04:05.6159440Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T21:04:05.6159660Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T21:04:05.6159878Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T21:04:05.6160097Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T21:04:05.6160317Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T21:04:05.6160538Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T21:04:05.6160766Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T21:04:05.6160975Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T21:04:05.6161200Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T21:04:05.6161403Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T21:04:05.6161626Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T21:04:05.6161834Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T21:04:05.6162055Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T21:04:05.6162304Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T21:04:05.6162663Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:04:05.6163009Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:04:05.6163357Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:04:05.6163702Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:04:05.6164041Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:04:05.6164376Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:04:05.6164685Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:04:05.6165051Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:04:05.6165404Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:04:05.6165777Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:04:05.6166117Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:04:05.6166194Z 2025-03-04T21:04:05.6166482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6167018Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6167111Z 2025-03-04T21:04:05.6167386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6169092Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6169169Z 2025-03-04T21:04:05.6169452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:04:05.6169598Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:04:05.6169663Z 2025-03-04T21:04:05.6170022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:04:05.6170256Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:04:05.6170330Z 2025-03-04T21:04:05.6170583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6171085Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6171153Z 2025-03-04T21:04:05.6171433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6173227Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6173320Z 2025-03-04T21:04:05.6173623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6173782Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:04:05.6173855Z 2025-03-04T21:04:05.6174110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6174665Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6174735Z 2025-03-04T21:04:05.6175012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6176869Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6176940Z 2025-03-04T21:04:05.6177241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6177387Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:04:05.6177463Z 2025-03-04T21:04:05.6177737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6178251Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6178319Z 2025-03-04T21:04:05.6178592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6180455Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6180546Z 2025-03-04T21:04:05.6180811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6181324Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:04:05.6181392Z 2025-03-04T21:04:05.6181666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6183554Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6183632Z 2025-03-04T21:04:05.6183923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6184074Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:04:05.6184151Z 2025-03-04T21:04:05.6184437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6184612Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:04:05.6184679Z 2025-03-04T21:04:05.6184941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6185430Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6185527Z 2025-03-04T21:04:05.6185800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6187595Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6187684Z 2025-03-04T21:04:05.6187985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6188321Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:04:05.6188393Z 2025-03-04T21:04:05.6188654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6189136Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6189214Z 2025-03-04T21:04:05.6189512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6191281Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6191359Z 2025-03-04T21:04:05.6191668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6191821Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:04:05.6191886Z 2025-03-04T21:04:05.6192138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6192625Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6192716Z 2025-03-04T21:04:05.6192980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6194736Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6194840Z 2025-03-04T21:04:05.6195117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6195282Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:04:05.6195348Z 2025-03-04T21:04:05.6195634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6195785Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:04:05.6195856Z 2025-03-04T21:04:05.6196117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6196605Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6196678Z 2025-03-04T21:04:05.6196943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6198725Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6198800Z 2025-03-04T21:04:05.6199085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6199248Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:04:05.6199312Z 2025-03-04T21:04:05.6199567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6200054Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6200125Z 2025-03-04T21:04:05.6200388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6202162Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6202236Z 2025-03-04T21:04:05.6202534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6202677Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:04:05.6202741Z 2025-03-04T21:04:05.6202994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6203480Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6203551Z 2025-03-04T21:04:05.6203811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6205586Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6205674Z 2025-03-04T21:04:05.6205954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6206118Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:04:05.6206186Z 2025-03-04T21:04:05.6206473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6206630Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:04:05.6206705Z 2025-03-04T21:04:05.6206956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6207465Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6207531Z 2025-03-04T21:04:05.6207801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6209580Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6209648Z 2025-03-04T21:04:05.6209933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6210075Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:04:05.6210147Z 2025-03-04T21:04:05.6210391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6210883Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6210970Z 2025-03-04T21:04:05.6211233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6212982Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6213069Z 2025-03-04T21:04:05.6213349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6213497Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:04:05.6213577Z 2025-03-04T21:04:05.6213838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6214377Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6214454Z 2025-03-04T21:04:05.6214715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6216521Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6216595Z 2025-03-04T21:04:05.6216850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6217381Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:04:05.6217447Z 2025-03-04T21:04:05.6217747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6219598Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6219689Z 2025-03-04T21:04:05.6219987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6220141Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:04:05.6220219Z 2025-03-04T21:04:05.6220534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6220698Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:04:05.6220763Z 2025-03-04T21:04:05.6221033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6221565Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6221637Z 2025-03-04T21:04:05.6221914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6223731Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6223811Z 2025-03-04T21:04:05.6224107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6224260Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:04:05.6224327Z 2025-03-04T21:04:05.6224604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6225114Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6225182Z 2025-03-04T21:04:05.6225461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6227306Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6227395Z 2025-03-04T21:04:05.6227701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6227845Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:04:05.6227919Z 2025-03-04T21:04:05.6228171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6228683Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6228750Z 2025-03-04T21:04:05.6229025Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6230838Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6230916Z 2025-03-04T21:04:05.6231205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6231379Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:04:05.6231450Z 2025-03-04T21:04:05.6231748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6231903Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:04:05.6231968Z 2025-03-04T21:04:05.6232224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6232717Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6232791Z 2025-03-04T21:04:05.6233049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6234794Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6234884Z 2025-03-04T21:04:05.6235161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6235306Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:04:05.6235370Z 2025-03-04T21:04:05.6235624Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6236121Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6236191Z 2025-03-04T21:04:05.6236452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6238201Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6238278Z 2025-03-04T21:04:05.6238555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6238702Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:04:05.6238787Z 2025-03-04T21:04:05.6239041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6239529Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6239594Z 2025-03-04T21:04:05.6239857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6241586Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6241679Z 2025-03-04T21:04:05.6241961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6242118Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:04:05.6242232Z 2025-03-04T21:04:05.6242508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6242665Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:04:05.6242729Z 2025-03-04T21:04:05.6242983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6243463Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6243538Z 2025-03-04T21:04:05.6243800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6245585Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6245674Z 2025-03-04T21:04:05.6245957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6246104Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:04:05.6246169Z 2025-03-04T21:04:05.6246424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6246910Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6246998Z 2025-03-04T21:04:05.6247262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6249048Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6249124Z 2025-03-04T21:04:05.6249408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6249553Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:04:05.6249617Z 2025-03-04T21:04:05.6249873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6250359Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6250431Z 2025-03-04T21:04:05.6250726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6252466Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6252552Z 2025-03-04T21:04:05.6252824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6252981Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:04:05.6253045Z 2025-03-04T21:04:05.6253330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6253505Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:04:05.6253571Z 2025-03-04T21:04:05.6253826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6254359Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6254435Z 2025-03-04T21:04:05.6254694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6256453Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6256529Z 2025-03-04T21:04:05.6256812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6256958Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:04:05.6257023Z 2025-03-04T21:04:05.6257289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6257770Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6257842Z 2025-03-04T21:04:05.6258104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6259846Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6259948Z 2025-03-04T21:04:05.6260232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6260375Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:04:05.6260441Z 2025-03-04T21:04:05.6260695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6261179Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6261252Z 2025-03-04T21:04:05.6261514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6263292Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6263368Z 2025-03-04T21:04:05.6263615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6264110Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:04:05.6264174Z 2025-03-04T21:04:05.6264441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6266230Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6266313Z 2025-03-04T21:04:05.6266608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6266749Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:04:05.6266820Z 2025-03-04T21:04:05.6267096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6267247Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:04:05.6267312Z 2025-03-04T21:04:05.6267565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6268041Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6268109Z 2025-03-04T21:04:05.6268388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6270109Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6270183Z 2025-03-04T21:04:05.6270484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6270620Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:04:05.6270690Z 2025-03-04T21:04:05.6270936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6271412Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6271496Z 2025-03-04T21:04:05.6271765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6273498Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6273591Z 2025-03-04T21:04:05.6273881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6274016Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:04:05.6274088Z 2025-03-04T21:04:05.6274335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6274843Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6274911Z 2025-03-04T21:04:05.6275183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6276942Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6277012Z 2025-03-04T21:04:05.6277292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6277438Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:04:05.6277509Z 2025-03-04T21:04:05.6277787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6277956Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:04:05.6278022Z 2025-03-04T21:04:05.6278278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6278747Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6278819Z 2025-03-04T21:04:05.6279079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6280814Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6280905Z 2025-03-04T21:04:05.6281182Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6281341Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:04:05.6281407Z 2025-03-04T21:04:05.6281663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6282148Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6282213Z 2025-03-04T21:04:05.6282480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6284222Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6284298Z 2025-03-04T21:04:05.6284600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6284732Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:04:05.6284805Z 2025-03-04T21:04:05.6285050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6285531Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6285596Z 2025-03-04T21:04:05.6285881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6287642Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6287716Z 2025-03-04T21:04:05.6288017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6288271Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:04:05.6288350Z 2025-03-04T21:04:05.6288634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6288782Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:04:05.6288848Z 2025-03-04T21:04:05.6289107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6289589Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6289663Z 2025-03-04T21:04:05.6289958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6291698Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6291794Z 2025-03-04T21:04:05.6292073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6292216Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:04:05.6292279Z 2025-03-04T21:04:05.6292533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6293032Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6293105Z 2025-03-04T21:04:05.6293366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6295215Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6295300Z 2025-03-04T21:04:05.6295591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6295732Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:04:05.6295797Z 2025-03-04T21:04:05.6296068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6296630Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6296697Z 2025-03-04T21:04:05.6296980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6298785Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6298875Z 2025-03-04T21:04:05.6299165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6299338Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:04:05.6299415Z 2025-03-04T21:04:05.6299711Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6299863Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:04:05.6299929Z 2025-03-04T21:04:05.6300202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6300738Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6300810Z 2025-03-04T21:04:05.6301086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6302899Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6302977Z 2025-03-04T21:04:05.6303279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6303442Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:04:05.6303509Z 2025-03-04T21:04:05.6303777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6304306Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6304383Z 2025-03-04T21:04:05.6304676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6306489Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6306584Z 2025-03-04T21:04:05.6306886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6307027Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:04:05.6307093Z 2025-03-04T21:04:05.6307353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6307847Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6307923Z 2025-03-04T21:04:05.6308204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6310022Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6310099Z 2025-03-04T21:04:05.6310394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6310551Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:04:05.6310615Z 2025-03-04T21:04:05.6310921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6311063Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:04:05.6311137Z 2025-03-04T21:04:05.6311405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6311903Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6311986Z 2025-03-04T21:04:05.6312247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6313985Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6314074Z 2025-03-04T21:04:05.6314352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6314490Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:04:05.6314557Z 2025-03-04T21:04:05.6314821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6315300Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6315373Z 2025-03-04T21:04:05.6315634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6317398Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6317478Z 2025-03-04T21:04:05.6317758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6317911Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:04:05.6317977Z 2025-03-04T21:04:05.6318231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6318705Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6318777Z 2025-03-04T21:04:05.6319035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6320784Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6320858Z 2025-03-04T21:04:05.6321129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6321295Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:04:05.6321358Z 2025-03-04T21:04:05.6321643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6321783Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:04:05.6321853Z 2025-03-04T21:04:05.6322105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6322589Z x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6322657Z 2025-03-04T21:04:05.6322926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6324720Z x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6324808Z 2025-03-04T21:04:05.6325098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6325232Z out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:04:05.6325304Z 2025-03-04T21:04:05.6325553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6326036Z x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6326124Z 2025-03-04T21:04:05.6326387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6328155Z x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6328229Z 2025-03-04T21:04:05.6328507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6328645Z out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:04:05.6328709Z 2025-03-04T21:04:05.6328962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6329442Z x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6329514Z 2025-03-04T21:04:05.6329789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6331536Z x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6331628Z 2025-03-04T21:04:05.6331905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6332055Z x_93 += out_51; out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T21:04:05.6332121Z 2025-03-04T21:04:05.6332417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6332558Z out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:04:05.6332629Z 2025-03-04T21:04:05.6332872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6333343Z x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6333408Z 2025-03-04T21:04:05.6333674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6335544Z x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6335621Z 2025-03-04T21:04:05.6335928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6336066Z out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T21:04:05.6336139Z 2025-03-04T21:04:05.6336402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6336921Z x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6336986Z 2025-03-04T21:04:05.6337276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6339074Z x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6339159Z 2025-03-04T21:04:05.6339473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6339612Z out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:04:05.6339689Z 2025-03-04T21:04:05.6339963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6340498Z x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6340579Z 2025-03-04T21:04:05.6340865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6342700Z x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6342776Z 2025-03-04T21:04:05.6343066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6343236Z x_99 += out_55; out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T21:04:05.6343302Z 2025-03-04T21:04:05.6343602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6343744Z out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:04:05.6343817Z 2025-03-04T21:04:05.6344078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6344634Z x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6344699Z 2025-03-04T21:04:05.6344983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6346790Z x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6346917Z 2025-03-04T21:04:05.6347223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6347365Z out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T21:04:05.6347441Z 2025-03-04T21:04:05.6347690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6348212Z x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6348278Z 2025-03-04T21:04:05.6348567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6350388Z x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6350458Z 2025-03-04T21:04:05.6350775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6350910Z out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:04:05.6350995Z 2025-03-04T21:04:05.6351245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6351733Z x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6351797Z 2025-03-04T21:04:05.6352063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6353798Z x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6353881Z 2025-03-04T21:04:05.6354160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6354315Z x_105 += out_59; out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T21:04:05.6354388Z 2025-03-04T21:04:05.6354677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6354827Z out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:04:05.6354889Z 2025-03-04T21:04:05.6355142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6355619Z x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6355684Z 2025-03-04T21:04:05.6355953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6357735Z x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6357824Z 2025-03-04T21:04:05.6358107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6358243Z out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T21:04:05.6358315Z 2025-03-04T21:04:05.6358558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6359032Z x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6359117Z 2025-03-04T21:04:05.6359387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6361176Z x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6361254Z 2025-03-04T21:04:05.6361541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6361677Z out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T21:04:05.6361749Z 2025-03-04T21:04:05.6361992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6362474Z x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6362542Z 2025-03-04T21:04:05.6362807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6364560Z x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6364643Z 2025-03-04T21:04:05.6364926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6365080Z x_111 += out_63; out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T21:04:05.6365155Z 2025-03-04T21:04:05.6365435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6365599Z out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T21:04:05.6365665Z 2025-03-04T21:04:05.6365923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6366399Z x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6366472Z 2025-03-04T21:04:05.6366731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6368490Z x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6368567Z 2025-03-04T21:04:05.6368845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6368993Z out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T21:04:05.6369057Z 2025-03-04T21:04:05.6369309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6369808Z x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6369873Z 2025-03-04T21:04:05.6370137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6371883Z x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6371983Z 2025-03-04T21:04:05.6372273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6372411Z out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T21:04:05.6372482Z 2025-03-04T21:04:05.6372731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6373219Z x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6373283Z 2025-03-04T21:04:05.6373551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6375462Z x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6375550Z 2025-03-04T21:04:05.6375850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6375999Z x_117 += out_67; out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T21:04:05.6376088Z 2025-03-04T21:04:05.6376370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6376516Z out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T21:04:05.6376579Z 2025-03-04T21:04:05.6376837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6377334Z x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6377423Z 2025-03-04T21:04:05.6377690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6379505Z x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6379603Z 2025-03-04T21:04:05.6379895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6380045Z out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T21:04:05.6380114Z 2025-03-04T21:04:05.6380375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6380893Z x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6380966Z 2025-03-04T21:04:05.6381242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6383067Z x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6383143Z 2025-03-04T21:04:05.6383428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6383574Z out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T21:04:05.6383648Z 2025-03-04T21:04:05.6383898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6384411Z x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6384478Z 2025-03-04T21:04:05.6384751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6386546Z x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6386637Z 2025-03-04T21:04:05.6386926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6387078Z x_123 += out_71; out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T21:04:05.6387153Z 2025-03-04T21:04:05.6387449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6387603Z out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T21:04:05.6387670Z 2025-03-04T21:04:05.6387929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6388516Z x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6388598Z 2025-03-04T21:04:05.6388864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6390708Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6390806Z 2025-03-04T21:04:05.6391088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6391233Z out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T21:04:05.6391297Z 2025-03-04T21:04:05.6391553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6392027Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6392124Z 2025-03-04T21:04:05.6392392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6394243Z x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6394325Z 2025-03-04T21:04:05.6394618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6394768Z out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T21:04:05.6394835Z 2025-03-04T21:04:05.6395115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6395604Z x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6395681Z 2025-03-04T21:04:05.6395958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6397766Z x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6397857Z 2025-03-04T21:04:05.6398148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6398300Z x_129 += out_75; out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T21:04:05.6398373Z 2025-03-04T21:04:05.6398657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6398836Z out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T21:04:05.6398902Z 2025-03-04T21:04:05.6399167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6399649Z x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6399726Z 2025-03-04T21:04:05.6399991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6401818Z x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6401894Z 2025-03-04T21:04:05.6402185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6402335Z out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T21:04:05.6402400Z 2025-03-04T21:04:05.6402663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6403182Z x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6403259Z 2025-03-04T21:04:05.6403532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6405324Z x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6405425Z 2025-03-04T21:04:05.6405706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6405852Z out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T21:04:05.6405914Z 2025-03-04T21:04:05.6406169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6406651Z x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6406726Z 2025-03-04T21:04:05.6406987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6408762Z x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6408843Z 2025-03-04T21:04:05.6409119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6409275Z x_135 += out_79; out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T21:04:05.6409338Z 2025-03-04T21:04:05.6409634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6409774Z out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T21:04:05.6409845Z 2025-03-04T21:04:05.6410088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6410567Z x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6410654Z 2025-03-04T21:04:05.6410914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6412680Z x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6412770Z 2025-03-04T21:04:05.6413050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6413193Z out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T21:04:05.6413259Z 2025-03-04T21:04:05.6413512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6414008Z x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6414080Z 2025-03-04T21:04:05.6414388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6416220Z x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6416296Z 2025-03-04T21:04:05.6416587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6416733Z out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T21:04:05.6416799Z 2025-03-04T21:04:05.6417050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6417546Z x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6417618Z 2025-03-04T21:04:05.6417876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6419650Z x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6419738Z 2025-03-04T21:04:05.6420012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6420171Z x_141 += out_83; out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T21:04:05.6420237Z 2025-03-04T21:04:05.6420543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6420685Z out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T21:04:05.6420757Z 2025-03-04T21:04:05.6421009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6421490Z x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6421556Z 2025-03-04T21:04:05.6421822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6423592Z x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6423720Z 2025-03-04T21:04:05.6424013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6424152Z out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T21:04:05.6424226Z 2025-03-04T21:04:05.6424471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6424957Z x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6425046Z 2025-03-04T21:04:05.6425310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6427090Z x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6427168Z 2025-03-04T21:04:05.6427454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6427597Z out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T21:04:05.6427661Z 2025-03-04T21:04:05.6427915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6428395Z x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6428472Z 2025-03-04T21:04:05.6428737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6430504Z x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6430593Z 2025-03-04T21:04:05.6430869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6431024Z x_147 += out_87; out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T21:04:05.6431089Z 2025-03-04T21:04:05.6431377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6431533Z out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T21:04:05.6431605Z 2025-03-04T21:04:05.6431854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6432336Z x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6432401Z 2025-03-04T21:04:05.6432670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6434446Z x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6434513Z 2025-03-04T21:04:05.6434801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6434942Z out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T21:04:05.6435013Z 2025-03-04T21:04:05.6435263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6435765Z x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6435836Z 2025-03-04T21:04:05.6436096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6437864Z x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6437961Z 2025-03-04T21:04:05.6438246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6438388Z out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T21:04:05.6438453Z 2025-03-04T21:04:05.6438707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6439180Z x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6439251Z 2025-03-04T21:04:05.6439509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6441259Z x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6441336Z 2025-03-04T21:04:05.6441611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6441767Z x_153 += out_91; out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T21:04:05.6441830Z 2025-03-04T21:04:05.6442130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6442270Z out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T21:04:05.6442340Z 2025-03-04T21:04:05.6442585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6443060Z x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6467698Z 2025-03-04T21:04:05.6468206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6470089Z x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6470252Z 2025-03-04T21:04:05.6470576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6470734Z out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T21:04:05.6470803Z 2025-03-04T21:04:05.6471073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6471615Z x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6471688Z 2025-03-04T21:04:05.6471962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6473854Z x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6473932Z 2025-03-04T21:04:05.6474237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6474395Z out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T21:04:05.6474464Z 2025-03-04T21:04:05.6474733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6475307Z x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6475380Z 2025-03-04T21:04:05.6475657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6477473Z x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6477563Z 2025-03-04T21:04:05.6477853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6478019Z x_159 += out_95; out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T21:04:05.6478087Z 2025-03-04T21:04:05.6478402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6478552Z out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T21:04:05.6478624Z 2025-03-04T21:04:05.6478883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6479382Z x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6479445Z 2025-03-04T21:04:05.6479720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6481530Z x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6481628Z 2025-03-04T21:04:05.6481919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6482067Z out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T21:04:05.6482139Z 2025-03-04T21:04:05.6482391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6482890Z x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6482972Z 2025-03-04T21:04:05.6483237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6484994Z x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6485062Z 2025-03-04T21:04:05.6485356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6485501Z out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T21:04:05.6485564Z 2025-03-04T21:04:05.6485823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6486308Z x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6486381Z 2025-03-04T21:04:05.6486649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6488660Z x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6488763Z 2025-03-04T21:04:05.6489052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6489219Z x_165 += out_99; out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T21:04:05.6489286Z 2025-03-04T21:04:05.6489583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6489734Z out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T21:04:05.6489861Z 2025-03-04T21:04:05.6490118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6490623Z x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6490689Z 2025-03-04T21:04:05.6490966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6492779Z x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6492851Z 2025-03-04T21:04:05.6493151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6493293Z out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T21:04:05.6493371Z 2025-03-04T21:04:05.6493625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6494293Z x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6494376Z 2025-03-04T21:04:05.6494690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6496634Z x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6496738Z 2025-03-04T21:04:05.6497054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6497207Z out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T21:04:05.6497289Z 2025-03-04T21:04:05.6497561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6498096Z x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6498176Z 2025-03-04T21:04:05.6498460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6500363Z x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6500454Z 2025-03-04T21:04:05.6500763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6500949Z x_171 += out_103; out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T21:04:05.6501019Z 2025-03-04T21:04:05.6501391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6501552Z out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T21:04:05.6501630Z 2025-03-04T21:04:05.6501895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6502420Z x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6502509Z 2025-03-04T21:04:05.6502803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6504676Z x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6504778Z 2025-03-04T21:04:05.6505098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6505239Z out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T21:04:05.6505315Z 2025-03-04T21:04:05.6505564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6506073Z x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6506141Z 2025-03-04T21:04:05.6506417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6508243Z x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6508313Z 2025-03-04T21:04:05.6508608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6508748Z out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T21:04:05.6508828Z 2025-03-04T21:04:05.6509080Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6509596Z x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6509663Z 2025-03-04T21:04:05.6509935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6511668Z x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6511751Z 2025-03-04T21:04:05.6512035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6512194Z x_177 += out_107; out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T21:04:05.6512272Z 2025-03-04T21:04:05.6512560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6512716Z out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T21:04:05.6512782Z 2025-03-04T21:04:05.6513040Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6513525Z x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6513591Z 2025-03-04T21:04:05.6513857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6515630Z x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6515719Z 2025-03-04T21:04:05.6516010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6516150Z out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T21:04:05.6516221Z 2025-03-04T21:04:05.6516467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6516957Z x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6517040Z 2025-03-04T21:04:05.6517312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6519072Z x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6519148Z 2025-03-04T21:04:05.6519433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6519568Z out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T21:04:05.6519640Z 2025-03-04T21:04:05.6519886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6520377Z x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6520447Z 2025-03-04T21:04:05.6520722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6522868Z x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6522955Z 2025-03-04T21:04:05.6523250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6523415Z x_183 += out_111; out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T21:04:05.6523494Z 2025-03-04T21:04:05.6523803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6523982Z out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T21:04:05.6524050Z 2025-03-04T21:04:05.6524309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6524784Z x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6524859Z 2025-03-04T21:04:05.6525131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6526913Z x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6526994Z 2025-03-04T21:04:05.6527292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6527446Z out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T21:04:05.6527521Z 2025-03-04T21:04:05.6527778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6528301Z x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6528372Z 2025-03-04T21:04:05.6528650Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6530436Z x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6530543Z 2025-03-04T21:04:05.6530842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6530984Z out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T21:04:05.6531058Z 2025-03-04T21:04:05.6531313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6531819Z x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6531887Z 2025-03-04T21:04:05.6532162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6533991Z x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6534063Z 2025-03-04T21:04:05.6534473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6534662Z x_189 += out_115; out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T21:04:05.6534747Z 2025-03-04T21:04:05.6535099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6535278Z out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T21:04:05.6535352Z 2025-03-04T21:04:05.6535648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6536139Z x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6536233Z 2025-03-04T21:04:05.6536507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6538306Z x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6538400Z 2025-03-04T21:04:05.6538680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6538828Z out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191); x_191 = None 2025-03-04T21:04:05.6538895Z 2025-03-04T21:04:05.6539150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6539638Z x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6539717Z 2025-03-04T21:04:05.6539991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6541771Z x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6541852Z 2025-03-04T21:04:05.6542139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6542286Z out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193); x_193 = None 2025-03-04T21:04:05.6542360Z 2025-03-04T21:04:05.6542615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6543136Z x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6543203Z 2025-03-04T21:04:05.6543479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6545244Z x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6545337Z 2025-03-04T21:04:05.6545600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6546111Z x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:04:05.6546189Z 2025-03-04T21:04:05.6546463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6548340Z x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6548419Z 2025-03-04T21:04:05.6548701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6548865Z x_195 += x_197; out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195; x_195 = x_197 = None 2025-03-04T21:04:05.6548931Z 2025-03-04T21:04:05.6549223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6549372Z out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122); out_122 = None 2025-03-04T21:04:05.6549468Z 2025-03-04T21:04:05.6549721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6550211Z x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6550278Z 2025-03-04T21:04:05.6550555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6552358Z x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6552448Z 2025-03-04T21:04:05.6552746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6552902Z out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199); x_199 = None 2025-03-04T21:04:05.6552977Z 2025-03-04T21:04:05.6553236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6553728Z x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6553800Z 2025-03-04T21:04:05.6554071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6555855Z x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6555933Z 2025-03-04T21:04:05.6556230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6556374Z out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201); x_201 = None 2025-03-04T21:04:05.6556439Z 2025-03-04T21:04:05.6556693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6557173Z x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6557247Z 2025-03-04T21:04:05.6557513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6559325Z x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6559400Z 2025-03-04T21:04:05.6559692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6559857Z x_203 += out_123; out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203; x_203 = out_123 = None 2025-03-04T21:04:05.6559924Z 2025-03-04T21:04:05.6560206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6560347Z out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126); out_126 = None 2025-03-04T21:04:05.6560419Z 2025-03-04T21:04:05.6560665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6561142Z x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.6561208Z 2025-03-04T21:04:05.6561493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6563243Z x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6563330Z 2025-03-04T21:04:05.6563628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6563765Z out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205); x_205 = None 2025-03-04T21:04:05.6563840Z 2025-03-04T21:04:05.6564091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6564608Z x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.6564676Z 2025-03-04T21:04:05.6564954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6566771Z x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6566840Z 2025-03-04T21:04:05.6567135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6567270Z out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207); x_207 = None 2025-03-04T21:04:05.6567346Z 2025-03-04T21:04:05.6567598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6568121Z x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.6568195Z 2025-03-04T21:04:05.6568466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.6570236Z x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.6570327Z 2025-03-04T21:04:05.6570610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.6570796Z x_209 += out_127; out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209; x_209 = out_127 = None 2025-03-04T21:04:05.6570864Z 2025-03-04T21:04:05.6571155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.6571301Z out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130); out_130 = None 2025-03-04T21:04:05.6571377Z 2025-03-04T21:04:05.6571625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6572203Z x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T21:04:05.6572271Z 2025-03-04T21:04:05.6572531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6573086Z x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T21:04:05.6573163Z 2025-03-04T21:04:05.6573584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:04:05.6573865Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest'); x_210 = None 2025-03-04T21:04:05.6573934Z 2025-03-04T21:04:05.6574266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6574921Z x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T21:04:05.6574992Z 2025-03-04T21:04:05.6575375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:04:05.6575583Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features; x_212 = top_down_features = None 2025-03-04T21:04:05.6575671Z 2025-03-04T21:04:05.6575939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6576519Z x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T21:04:05.6576585Z 2025-03-04T21:04:05.6576999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:04:05.6577327Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T21:04:05.6577421Z 2025-03-04T21:04:05.6577677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6578262Z x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T21:04:05.6578337Z 2025-03-04T21:04:05.6578686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:04:05.6578904Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1; x_214 = top_down_features_1 = None 2025-03-04T21:04:05.6578973Z 2025-03-04T21:04:05.6579250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6579833Z x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T21:04:05.6579906Z 2025-03-04T21:04:05.6580312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:04:05.6580647Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T21:04:05.6580716Z 2025-03-04T21:04:05.6580975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6581609Z x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T21:04:05.6581684Z 2025-03-04T21:04:05.6582035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:04:05.6582250Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2; x_216 = top_down_features_2 = None 2025-03-04T21:04:05.6582338Z 2025-03-04T21:04:05.6582591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6583214Z x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T21:04:05.6583282Z 2025-03-04T21:04:05.6583655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T21:04:05.6583888Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T21:04:05.6583964Z 2025-03-04T21:04:05.6584410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.6584578Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:04:05.6584646Z 2025-03-04T21:04:05.6584954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.6585099Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:04:05.6585173Z 2025-03-04T21:04:05.6585613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.6585808Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:04:05.6585874Z 2025-03-04T21:04:05.6586182Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.6586329Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:04:05.6586402Z 2025-03-04T21:04:05.6586788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:05.6586982Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:04:05.6587087Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:04:05.6587223Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:04:05.6587289Z 2025-03-04T21:04:05.6587635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:05.6587788Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:04:05.6587855Z 2025-03-04T21:04:05.6588318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:05.6588452Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:04:05.6588525Z 2025-03-04T21:04:05.6588898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:05.6589162Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:04:05.6589227Z 2025-03-04T21:04:05.6589664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:05.6589797Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:04:05.6590242Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:04:05.6590401Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:04:05.6590533Z x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:04:05.6590598Z 2025-03-04T21:04:05.6591043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.6591197Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:04:05.6591271Z 2025-03-04T21:04:05.6591570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.6591724Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:04:05.6591790Z 2025-03-04T21:04:05.6592234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.6592420Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:04:05.6592496Z 2025-03-04T21:04:05.6592793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.6592943Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:04:05.6593007Z 2025-03-04T21:04:05.6593391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:05.6593592Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:04:05.6593709Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:04:05.6593839Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:04:05.6593912Z 2025-03-04T21:04:05.6594278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:05.6594418Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:04:05.6594483Z 2025-03-04T21:04:05.6594816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:05.6594948Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:04:05.6595013Z 2025-03-04T21:04:05.6595402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:05.6595640Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:04:05.6595712Z 2025-03-04T21:04:05.6596128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:05.6596270Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:04:05.6596687Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:04:05.6596836Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:04:05.6596956Z x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:04:05.6597028Z 2025-03-04T21:04:05.6597458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.6597615Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:04:05.6597682Z 2025-03-04T21:04:05.6597982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.6598122Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:04:05.6598197Z 2025-03-04T21:04:05.6598650Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.6598806Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:04:05.6598873Z 2025-03-04T21:04:05.6599175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.6599314Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:04:05.6599388Z 2025-03-04T21:04:05.6599758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:05.6599965Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:04:05.6600070Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:04:05.6600202Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:04:05.6600269Z 2025-03-04T21:04:05.6600624Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:05.6600760Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:04:05.6600826Z 2025-03-04T21:04:05.6601159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:05.6601283Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:04:05.6601359Z 2025-03-04T21:04:05.6601740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:05.6601980Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:04:05.6602046Z 2025-03-04T21:04:05.6602472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:05.6602606Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:04:05.6603033Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:04:05.6603172Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:04:05.6603297Z x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T21:04:05.6603362Z 2025-03-04T21:04:05.6603794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.6603939Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:04:05.6604012Z 2025-03-04T21:04:05.6604306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.6604450Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:04:05.6604517Z 2025-03-04T21:04:05.6604966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.6605111Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:04:05.6605185Z 2025-03-04T21:04:05.6605481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.6605625Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:04:05.6605691Z 2025-03-04T21:04:05.6606076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:05.6606272Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:04:05.6606386Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:04:05.6606505Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:04:05.6606580Z 2025-03-04T21:04:05.6606925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:05.6607060Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:04:05.6607125Z 2025-03-04T21:04:05.6607457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:05.6607589Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:04:05.6607673Z 2025-03-04T21:04:05.6608062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:05.6608275Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:04:05.6608348Z 2025-03-04T21:04:05.6608758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:05.6608892Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:04:05.6609309Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:04:05.6609465Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:04:05.6609582Z x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T21:04:05.6609656Z 2025-03-04T21:04:05.6610089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.6610244Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:04:05.6610309Z 2025-03-04T21:04:05.6610610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.6610749Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:04:05.6610822Z 2025-03-04T21:04:05.6611275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.6611426Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:04:05.6611493Z 2025-03-04T21:04:05.6611790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.6611923Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:04:05.6611994Z 2025-03-04T21:04:05.6612363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:05.6612568Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:04:05.6612670Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:04:05.6612796Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:04:05.6612880Z 2025-03-04T21:04:05.6613219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:05.6613345Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:04:05.6613418Z 2025-03-04T21:04:05.6613740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:05.6613866Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:04:05.6613967Z 2025-03-04T21:04:05.6614420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:05.6614652Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:04:05.6614721Z 2025-03-04T21:04:05.6615147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:05.6615279Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:04:05.6615717Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:04:05.6615864Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:04:05.6615990Z x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T21:04:05.6616058Z 2025-03-04T21:04:05.6616369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:04:05.6616500Z tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32); x_218 = None 2025-03-04T21:04:05.6616642Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32); x_219 = None 2025-03-04T21:04:05.6616766Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32); x_220 = None 2025-03-04T21:04:05.6616897Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32); x_221 = None 2025-03-04T21:04:05.6617019Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32); x_222 = None 2025-03-04T21:04:05.6617095Z 2025-03-04T21:04:05.6617370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6617891Z x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_217 = None 2025-03-04T21:04:05.6617959Z 2025-03-04T21:04:05.6618247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:05.6618446Z x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False); x_223 = None 2025-03-04T21:04:05.6618523Z 2025-03-04T21:04:05.6618910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:05.6619455Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:05.6619531Z 2025-03-04T21:04:05.6619899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:05.6620428Z x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_224 = None 2025-03-04T21:04:05.6620513Z 2025-03-04T21:04:05.6620779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6621259Z x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_215 = None 2025-03-04T21:04:05.6621333Z 2025-03-04T21:04:05.6621608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:05.6621840Z x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False); x_225 = None 2025-03-04T21:04:05.6621908Z 2025-03-04T21:04:05.6622293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:05.6622807Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:05.6622882Z 2025-03-04T21:04:05.6623246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:05.6623782Z x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_226 = None 2025-03-04T21:04:05.6623859Z 2025-03-04T21:04:05.6624117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6624602Z x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_213 = None 2025-03-04T21:04:05.6624668Z 2025-03-04T21:04:05.6624950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:05.6625139Z x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False); x_227 = None 2025-03-04T21:04:05.6625215Z 2025-03-04T21:04:05.6625591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:05.6626120Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:05.6626185Z 2025-03-04T21:04:05.6626549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:05.6627077Z x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_228 = None 2025-03-04T21:04:05.6627166Z 2025-03-04T21:04:05.6627433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6627908Z x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_211 = None 2025-03-04T21:04:05.6627982Z 2025-03-04T21:04:05.6628257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:05.6628467Z x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False); x_229 = None 2025-03-04T21:04:05.6628534Z 2025-03-04T21:04:05.6628916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:05.6629413Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:05.6629488Z 2025-03-04T21:04:05.6629847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:05.6630371Z x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_230 = None 2025-03-04T21:04:05.6630445Z 2025-03-04T21:04:05.6630701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.6631474Z x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:04:05.6631540Z 2025-03-04T21:04:05.6631822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:05.6632002Z x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False); x_231 = None 2025-03-04T21:04:05.6632074Z 2025-03-04T21:04:05.6632467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:05.6633331Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:04:05.6633420Z 2025-03-04T21:04:05.6633778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:05.6634605Z x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:04:05.6634671Z 2025-03-04T21:04:05.6635019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:04:05.6635205Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:04:05.6635358Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:04:05.6635526Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:04:05.6635678Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:04:05.6635835Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:04:05.6635981Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:04:05.6636129Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:04:05.6636273Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:04:05.6636420Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:04:05.6636576Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:04:05.6636644Z 2025-03-04T21:04:05.6637087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:04:05.6637272Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304); x_233 = None 2025-03-04T21:04:05.6637472Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:04:05.6637662Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:04:05.6637832Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152); x_234 = None 2025-03-04T21:04:05.6638021Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:04:05.6638211Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:04:05.6638373Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76); x_235 = None 2025-03-04T21:04:05.6638541Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:04:05.6638718Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:04:05.6638863Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38); x_236 = None 2025-03-04T21:04:05.6639052Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:04:05.6639220Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:04:05.6639372Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19); x_237 = None 2025-03-04T21:04:05.6639533Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:04:05.6639709Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:04:05.6639775Z 2025-03-04T21:04:05.6640196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:05.6640420Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:04:05.6640497Z 2025-03-04T21:04:05.6640939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:05.6641106Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:04:05.6641257Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:04:05.6641409Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:04:05.6641474Z 2025-03-04T21:04:05.6641858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:05.6642041Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:04:05.6642127Z 2025-03-04T21:04:05.6642456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:05.6642602Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:04:05.6642675Z 2025-03-04T21:04:05.6642991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:05.6643135Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:04:05.6643267Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:05.6643431Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:04:05.6643499Z 2025-03-04T21:04:05.6643829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:05.6643958Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:04:05.6644107Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:04:05.6644264Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:04:05.6644340Z 2025-03-04T21:04:05.6644655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:05.6644788Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:05.6644881Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:04:05.6645044Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:04:05.6645111Z 2025-03-04T21:04:05.6645430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:05.6645581Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:04:05.6645681Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:04:05.6645811Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:04:05.6645885Z 2025-03-04T21:04:05.6646226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:05.6646406Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:05.6646525Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:04:05.6646597Z 2025-03-04T21:04:05.6646898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:05.6647062Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:05.6647178Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:04:05.6647249Z 2025-03-04T21:04:05.6647548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:05.6647707Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:05.6647821Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:04:05.6647897Z 2025-03-04T21:04:05.6648211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:05.6648408Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:04:05.6648520Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:04:05.6648591Z 2025-03-04T21:04:05.6648929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:05.6649077Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:04:05.6649150Z 2025-03-04T21:04:05.6649492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:05.6649641Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:04:05.6649707Z 2025-03-04T21:04:05.6650081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:05.6650222Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:04:05.6650355Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:04:05.6650508Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:04:05.6650656Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:04:05.6650725Z 2025-03-04T21:04:05.6651081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:05.6651240Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:04:05.6651372Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:04:05.6651525Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:04:05.6651670Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:04:05.6651735Z 2025-03-04T21:04:05.6652081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:05.6652222Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:04:05.6652391Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:04:05.6652527Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:04:05.6652599Z 2025-03-04T21:04:05.6652935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:05.6653061Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:04:05.6653226Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:04:05.6653371Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:04:05.6653436Z 2025-03-04T21:04:05.6653755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:05.6653859Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:04:05.6654005Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:04:05.6654073Z 2025-03-04T21:04:05.6654486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:05.6654591Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:04:05.6654723Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:04:05.6654791Z 2025-03-04T21:04:05.6655119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:05.6655255Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:04:05.6655414Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:04:05.6655483Z 2025-03-04T21:04:05.6655803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:05.6655938Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:04:05.6656085Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:04:05.6656154Z 2025-03-04T21:04:05.6656532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:05.6656726Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:04:05.6656807Z 2025-03-04T21:04:05.6657152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:05.6657351Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:04:05.6657419Z 2025-03-04T21:04:05.6657820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:05.6658014Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:04:05.6658082Z 2025-03-04T21:04:05.6658501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:05.6658738Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:04:05.6658814Z 2025-03-04T21:04:05.6659256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:05.6659425Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:04:05.6659583Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:04:05.6659736Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:04:05.6659803Z 2025-03-04T21:04:05.6660191Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:05.6660369Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:04:05.6660460Z 2025-03-04T21:04:05.6660792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:05.6660958Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:04:05.6661027Z 2025-03-04T21:04:05.6661362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:05.6661504Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:04:05.6661648Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:04:05.6661813Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:04:05.6661892Z 2025-03-04T21:04:05.6662231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:05.6662374Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:04:05.6662524Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:04:05.6662698Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:04:05.6662767Z 2025-03-04T21:04:05.6663103Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:05.6663235Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:04:05.6663345Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:04:05.6663506Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:04:05.6663583Z 2025-03-04T21:04:05.6663913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:05.6664083Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:04:05.6664186Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:04:05.6664334Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:04:05.6664401Z 2025-03-04T21:04:05.6664728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:05.6664911Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:05.6665041Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:04:05.6665110Z 2025-03-04T21:04:05.6665437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:05.6665609Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:05.6665730Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:04:05.6665807Z 2025-03-04T21:04:05.6666123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:05.6666297Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:05.6666413Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:04:05.6666485Z 2025-03-04T21:04:05.6666807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:05.6667007Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:04:05.6667118Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:04:05.6667192Z 2025-03-04T21:04:05.6667530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:05.6667681Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:04:05.6667748Z 2025-03-04T21:04:05.6668091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:05.6668232Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:04:05.6668306Z 2025-03-04T21:04:05.6668676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:05.6668825Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:04:05.6668955Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:04:05.6669120Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:04:05.6669262Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:04:05.6669354Z 2025-03-04T21:04:05.6669705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:05.6669853Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:04:05.6669984Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:04:05.6670148Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:04:05.6670289Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:04:05.6670361Z 2025-03-04T21:04:05.6670694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:05.6670841Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:04:05.6671015Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:04:05.6671154Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:04:05.6671228Z 2025-03-04T21:04:05.6671563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:05.6671686Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:04:05.6671855Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:04:05.6671999Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:04:05.6672067Z 2025-03-04T21:04:05.6672385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:05.6672504Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:04:05.6672636Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:04:05.6672702Z 2025-03-04T21:04:05.6673020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:05.6673119Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:04:05.6673247Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:04:05.6673312Z 2025-03-04T21:04:05.6673627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:05.6673749Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:04:05.6673898Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:04:05.6673964Z 2025-03-04T21:04:05.6674294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:05.6674411Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:04:05.6674552Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:04:05.6674619Z 2025-03-04T21:04:05.6674982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:05.6675178Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:04:05.6675271Z 2025-03-04T21:04:05.6675610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:05.6675786Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:04:05.6675856Z 2025-03-04T21:04:05.6676250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:05.6676428Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:04:05.6676501Z 2025-03-04T21:04:05.6676906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:05.6677143Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:04:05.6677209Z 2025-03-04T21:04:05.6677658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:05.6677814Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:04:05.6677974Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:04:05.6678115Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:04:05.6678188Z 2025-03-04T21:04:05.6678562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:05.6678794Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:04:05.6678871Z 2025-03-04T21:04:05.6679188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:05.6679342Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:04:05.6679407Z 2025-03-04T21:04:05.6679730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:05.6679863Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:04:05.6679998Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:04:05.6680153Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:04:05.6680226Z 2025-03-04T21:04:05.6680549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:05.6680695Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:04:05.6680815Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:04:05.6680973Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:04:05.6681039Z 2025-03-04T21:04:05.6681356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:05.6681479Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:04:05.6681595Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:04:05.6681731Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:04:05.6681805Z 2025-03-04T21:04:05.6682114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:05.6682272Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:04:05.6682368Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:04:05.6682505Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:04:05.6682572Z 2025-03-04T21:04:05.6682881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:05.6683058Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:05.6683180Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:04:05.6683247Z 2025-03-04T21:04:05.6683558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:05.6683713Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:05.6683836Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:04:05.6683901Z 2025-03-04T21:04:05.6684207Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:05.6684360Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:05.6684482Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:04:05.6684563Z 2025-03-04T21:04:05.6684882Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:05.6685073Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:04:05.6685184Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:04:05.6685258Z 2025-03-04T21:04:05.6685587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:05.6685733Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:04:05.6685802Z 2025-03-04T21:04:05.6686134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:05.6686271Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:04:05.6686345Z 2025-03-04T21:04:05.6686691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:05.6686831Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:04:05.6686956Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:04:05.6687114Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:04:05.6687252Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:04:05.6687338Z 2025-03-04T21:04:05.6687681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:05.6687825Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:04:05.6687947Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:04:05.6688246Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:04:05.6688394Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:04:05.6688468Z 2025-03-04T21:04:05.6688793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:05.6688965Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:04:05.6689125Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:04:05.6689268Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:04:05.6689335Z 2025-03-04T21:04:05.6689668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:05.6689785Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:04:05.6689957Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:04:05.6690092Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:04:05.6690171Z 2025-03-04T21:04:05.6690516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:05.6690627Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:04:05.6690751Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:04:05.6690827Z 2025-03-04T21:04:05.6691136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:05.6691246Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:04:05.6691363Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:04:05.6691440Z 2025-03-04T21:04:05.6691744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:05.6691875Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:04:05.6692012Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:04:05.6692098Z 2025-03-04T21:04:05.6692417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:05.6692539Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:04:05.6692669Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:04:05.6692741Z 2025-03-04T21:04:05.6693085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:05.6693286Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:04:05.6693380Z 2025-03-04T21:04:05.6693715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:05.6693891Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:04:05.6693960Z 2025-03-04T21:04:05.6694425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:05.6694615Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:04:05.6694712Z 2025-03-04T21:04:05.6695143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:05.6695367Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:04:05.6695434Z 2025-03-04T21:04:05.6695890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:05.6696042Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:04:05.6696203Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:04:05.6696341Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:04:05.6696416Z 2025-03-04T21:04:05.6696803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:05.6696987Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:04:05.6697055Z 2025-03-04T21:04:05.6697378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:05.6697527Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:04:05.6697602Z 2025-03-04T21:04:05.6697914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:05.6698055Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:04:05.6698183Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:04:05.6698339Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:04:05.6698405Z 2025-03-04T21:04:05.6698745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:05.6698873Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:04:05.6699003Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:04:05.6699155Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:04:05.6699230Z 2025-03-04T21:04:05.6699538Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:05.6699686Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:04:05.6699781Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:04:05.6699924Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:04:05.6699991Z 2025-03-04T21:04:05.6700311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:05.6700460Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:04:05.6700567Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:04:05.6700699Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:04:05.6700790Z 2025-03-04T21:04:05.6701104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:05.6701263Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:05.6701388Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:04:05.6701454Z 2025-03-04T21:04:05.6701762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:05.6701917Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:05.6702042Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:04:05.6702109Z 2025-03-04T21:04:05.6702415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:05.6702574Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:05.6702707Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:04:05.6702775Z 2025-03-04T21:04:05.6703095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:05.6703282Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:04:05.6703405Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:04:05.6703473Z 2025-03-04T21:04:05.6703821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:05.6703969Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:04:05.6704047Z 2025-03-04T21:04:05.6704386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:05.6704550Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:04:05.6704616Z 2025-03-04T21:04:05.6704968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:05.6705104Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:04:05.6705238Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:04:05.6705395Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:04:05.6705562Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:04:05.6705631Z 2025-03-04T21:04:05.6705990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:05.6706128Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:04:05.6706261Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:04:05.6706419Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:04:05.6706558Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:04:05.6706647Z 2025-03-04T21:04:05.6706978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:05.6707107Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:04:05.6707271Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:04:05.6707417Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:04:05.6707483Z 2025-03-04T21:04:05.6707822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:05.6707938Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:04:05.6708112Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:04:05.6708247Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:04:05.6708323Z 2025-03-04T21:04:05.6708651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:05.6708760Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:04:05.6708881Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:04:05.6708954Z 2025-03-04T21:04:05.6709263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:05.6709365Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:04:05.6709481Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:04:05.6709554Z 2025-03-04T21:04:05.6709859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:05.6709985Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:04:05.6710123Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:04:05.6710198Z 2025-03-04T21:04:05.6710520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:05.6710644Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:04:05.6710776Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:04:05.6710849Z 2025-03-04T21:04:05.6711193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:05.6711420Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:04:05.6711487Z 2025-03-04T21:04:05.6711834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:05.6711999Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:04:05.6712076Z 2025-03-04T21:04:05.6712460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:05.6712643Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:04:05.6712732Z 2025-03-04T21:04:05.6713149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:05.6713360Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:04:05.6713436Z 2025-03-04T21:04:05.6713873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:05.6714030Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:04:05.6714190Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:04:05.6714332Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:04:05.6714406Z 2025-03-04T21:04:05.6714798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:05.6714974Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:04:05.6715040Z 2025-03-04T21:04:05.6715354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:05.6715503Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:04:05.6715575Z 2025-03-04T21:04:05.6715878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:05.6716015Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:04:05.6716141Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:04:05.6716292Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:04:05.6716356Z 2025-03-04T21:04:05.6716691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:05.6716813Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:04:05.6716937Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:04:05.6717082Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:04:05.6717152Z 2025-03-04T21:04:05.6717455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:05.6717600Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:04:05.6717690Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:04:05.6717827Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:04:05.6717891Z 2025-03-04T21:04:05.6718205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:05.6718349Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:04:05.6718447Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:04:05.6718573Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:04:05.6718661Z 2025-03-04T21:04:05.6718959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:05.6719119Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:05.6719230Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:04:05.6719305Z 2025-03-04T21:04:05.6719595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:05.6719748Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:05.6719858Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:04:05.6719932Z 2025-03-04T21:04:05.6720220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:05.6720388Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:05.6720498Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:04:05.6720570Z 2025-03-04T21:04:05.6720875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:05.6721053Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:04:05.6721167Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:04:05.6721231Z 2025-03-04T21:04:05.6721562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:05.6721703Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:04:05.6721774Z 2025-03-04T21:04:05.6722098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:05.6722254Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:04:05.6722319Z 2025-03-04T21:04:05.6722659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:05.6722792Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:04:05.6722922Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:04:05.6723076Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:04:05.6723234Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:04:05.6723297Z 2025-03-04T21:04:05.6723639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:05.6723771Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:04:05.6723896Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:04:05.6724042Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:04:05.6724183Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:04:05.6724263Z 2025-03-04T21:04:05.6724592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:05.6724709Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:04:05.6724872Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:04:05.6725003Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:04:05.6725073Z 2025-03-04T21:04:05.6725397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:05.6725517Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:04:05.6725678Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:04:05.6725817Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:04:05.6725882Z 2025-03-04T21:04:05.6726212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:05.6726314Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:04:05.6726436Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:04:05.6726500Z 2025-03-04T21:04:05.6726807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:05.6726900Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:04:05.6727022Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:04:05.6727088Z 2025-03-04T21:04:05.6727393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:05.6727509Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:04:05.6727648Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:04:05.6727728Z 2025-03-04T21:04:05.6728036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:05.6728147Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:04:05.6728281Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:04:05.6728344Z 2025-03-04T21:04:05.6728689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:05.6728899Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:04:05.6728965Z 2025-03-04T21:04:05.6729295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:05.6729451Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:04:05.6729523Z 2025-03-04T21:04:05.6729892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:05.6730065Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:04:05.6730144Z 2025-03-04T21:04:05.6730632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:04:05.6730766Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:04:05.6730839Z 2025-03-04T21:04:05.6731130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.6731278Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:04:05.6731342Z 2025-03-04T21:04:05.6731773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:05.6731889Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:04:05.6732015Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:04:05.6732130Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:04:05.6732201Z 2025-03-04T21:04:05.6732656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:05.6732794Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:05.6733021Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:04:05.6733096Z 2025-03-04T21:04:05.6733558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:05.6733735Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:05.6733802Z 2025-03-04T21:04:05.6734124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.6734321Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:04:05.6734403Z 2025-03-04T21:04:05.6734845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:05.6734978Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:04:05.6735122Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:04:05.6735256Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:04:05.6735324Z 2025-03-04T21:04:05.6735804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:05.6735961Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:05.6736200Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:04:05.6736275Z 2025-03-04T21:04:05.6736735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:05.6736929Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:05.6736998Z 2025-03-04T21:04:05.6737303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.6737432Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:04:05.6737505Z 2025-03-04T21:04:05.6737935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:05.6738057Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:04:05.6738166Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:04:05.6738295Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:04:05.6738359Z 2025-03-04T21:04:05.6738836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:05.6738973Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:05.6739216Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:04:05.6739282Z 2025-03-04T21:04:05.6739744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:05.6739910Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:05.6739987Z 2025-03-04T21:04:05.6740281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.6740430Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:04:05.6740495Z 2025-03-04T21:04:05.6740938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:05.6741053Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:04:05.6741165Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:04:05.6741283Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:04:05.6741373Z 2025-03-04T21:04:05.6741827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:05.6741971Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:05.6742216Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:04:05.6742283Z 2025-03-04T21:04:05.6742743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:05.6742923Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:05.6742999Z 2025-03-04T21:04:05.6743290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.6743425Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:04:05.6743490Z 2025-03-04T21:04:05.6743924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:05.6744037Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:04:05.6744149Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:04:05.6744265Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:04:05.6744341Z 2025-03-04T21:04:05.6744804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:05.6744983Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:04:05.6745220Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:04:05.6745295Z 2025-03-04T21:04:05.6745745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:05.6745919Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:05.6745988Z 2025-03-04T21:04:05.6746292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.6746419Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:04:05.6746492Z 2025-03-04T21:04:05.6746787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:05.6747173Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:04:05.6747239Z 2025-03-04T21:04:05.6747526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:05.6748000Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:04:05.6748081Z 2025-03-04T21:04:05.6748369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:05.6748575Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:04:05.6748646Z 2025-03-04T21:04:05.6749036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:04:05.6749204Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:04:05.6749272Z 2025-03-04T21:04:05.6749586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:04:05.6749736Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:04:05.6749812Z 2025-03-04T21:04:05.6750194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:04:05.6750348Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:04:05.6750412Z 2025-03-04T21:04:05.6750896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:04:05.6751035Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:04:05.6751178Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:04:05.6751332Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:04:05.6751472Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:04:05.6751536Z 2025-03-04T21:04:05.6751901Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:04:05.6752017Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:04:05.6752090Z 2025-03-04T21:04:05.6752104Z 2025-03-04T21:04:05.6752196Z class GraphModule(torch.nn.Module): 2025-03-04T21:04:05.6872403Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", 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L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 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L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 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L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 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L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:04:05.6873197Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:04:05.6873548Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6873947Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6874361Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6874734Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6875088Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6875449Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6875871Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6876278Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6876658Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6877038Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6877407Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6877816Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6878221Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6878596Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6878995Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6879344Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6879754Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6880158Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6880539Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6880931Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6881293Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:04:05.6881715Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6882143Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6882552Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6882939Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6883280Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6883703Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6884102Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6884487Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6884852Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6885220Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6885630Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6886023Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6886405Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6886775Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6887132Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6887547Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6887952Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6888457Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6888898Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6889250Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6889649Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6890051Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6890460Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6890830Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6891182Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6891580Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6892012Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6892399Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6892780Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6893130Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6893532Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6893953Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6894423Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6894870Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6895251Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6895691Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6896106Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6896492Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6896897Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6897252Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6897671Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6898064Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6898456Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6898854Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6899210Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6899629Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6900041Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6900438Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6900834Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6901219Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:04:05.6901636Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6902065Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6902466Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6902853Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6903208Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6903630Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6904035Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6904416Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6904786Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6905153Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6905552Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6905958Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6906338Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6906714Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6907078Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6907478Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6907881Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6908261Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6908654Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6908996Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6909398Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6909819Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6910193Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6910566Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6910904Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6911305Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6911709Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6912092Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6912466Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6912809Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6913221Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6913627Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6914008Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6914374Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6914740Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6915140Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6915536Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6915914Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6916307Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6916658Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6917055Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6917456Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6917855Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6918230Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6918580Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6918982Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6919389Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6919794Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6920164Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6920512Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6920907Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6921321Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6921703Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6922080Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6922429Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6922840Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6923244Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6923617Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6923990Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6924361Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6924772Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6925175Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6925556Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6925937Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6926311Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:04:05.6926731Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6927138Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6927558Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6927945Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6928288Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6928690Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6929100Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6929484Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6929853Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6930201Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6930625Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6931023Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6931412Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6931784Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6932134Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6932537Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6932951Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6933337Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6933713Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6934078Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6934550Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6934971Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6935365Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6935774Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6936124Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6936521Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6936931Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6937325Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6937699Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6938040Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6938445Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6938846Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6939231Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6939607Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6939960Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6940391Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6940821Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6941213Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6941598Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6941956Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6942368Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6942763Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6943147Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6943517Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6943886Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6944289Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6944682Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6945062Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6945431Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6945797Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6946202Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6946592Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6946994Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6947367Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6947718Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6948117Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6948541Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6948923Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6949288Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6949636Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6950048Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6950453Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6950828Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6951205Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6951560Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6951980Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6952386Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6952765Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6953147Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6953508Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6953919Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6954323Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6954710Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6955090Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6955436Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6955841Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6956237Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6956640Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6957013Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6957355Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6957755Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6958148Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6958545Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6958913Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6959257Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6959681Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6960081Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6960468Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6960841Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6961214Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6961624Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6962031Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6962418Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6962803Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6963159Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6963565Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6963970Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6964351Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6964734Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6965108Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6965508Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6965911Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6966305Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6966678Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6967016Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6967422Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6967845Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6968220Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6968595Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6968942Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6969370Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6969764Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6970150Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6970523Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6970872Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6971294Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6971689Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6972075Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6972450Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6972815Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6973232Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6973637Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6974048Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6974485Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6974850Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6975263Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6975678Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6976094Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6976513Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6976906Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6977339Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6977781Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6978232Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6978652Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6979051Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6979508Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6979929Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6980317Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6980714Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6981105Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6981535Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6981964Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6982355Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6982775Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6983137Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6983573Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6983998Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6984394Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6984810Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6985163Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6985588Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6985999Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6986424Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6986810Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6987170Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6987619Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6988050Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6988544Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6988920Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6989281Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6989735Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6990133Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6990519Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6990890Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6991248Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6991704Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6992114Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6992501Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6992900Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6993259Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6993664Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6994075Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6994476Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6994853Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6995204Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.6995602Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6996020Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6996400Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6996776Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6997122Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.6997532Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6997948Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.6998325Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.6998700Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.6999044Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.6999470Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.6999862Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7000248Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7000639Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7000989Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.7001399Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7001793Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7002179Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7002576Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7002922Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.7003324Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7003718Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7004107Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7004488Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7004839Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.7005243Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7005657Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7006046Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7006414Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7006763Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.7007175Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7007579Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7007965Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7008333Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7008703Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.7009100Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7009500Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7009896Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7010273Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7010667Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.7011073Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7011477Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7011861Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7012254Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7012598Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.7013006Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7013458Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7013850Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7014279Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7014646Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.7015068Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7015501Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7015915Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7016348Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7016751Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.7017217Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7017667Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7018102Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7018511Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7018933Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.7019398Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7019833Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7020270Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7020714Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7021100Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.7021524Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7021947Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7022373Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7022756Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7023114Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.7023538Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7023967Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7024383Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7024781Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7025146Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.7025573Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7026009Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7026396Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7026794Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7027165Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.7027597Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7028021Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7028417Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7028802Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7029174Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.7029610Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7030021Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7030401Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7030780Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7031142Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.7031556Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7031953Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7032363Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7032742Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7033088Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.7033493Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7033908Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7034293Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7034660Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7035009Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.7035433Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7035831Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7036217Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7036594Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7036951Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.7037368Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7037771Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7040405Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7040800Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7041172Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.7041571Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7041970Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7042415Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7042789Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7043148Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.7043560Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7043959Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7044347Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7044713Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7045064Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.7045468Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7045874Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7046273Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7046647Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7047065Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.7047478Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7047880Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7048256Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7048631Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7048996Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.7049399Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7049821Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7050199Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7050574Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7050917Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.7051321Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7051722Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7052102Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7052493Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7052842Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.7053288Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7053709Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7054112Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7054559Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7054929Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.7055357Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7055770Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7056159Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7056546Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7056903Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.7057321Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7057737Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7058126Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7058514Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7058865Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.7059300Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7059705Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7060119Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7060531Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7060887Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.7061306Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7061698Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7062099Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7062467Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7062815Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.7063220Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7063621Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7064003Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7064371Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7064720Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.7065121Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7065541Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7065926Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7066293Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7066675Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:04:05.7067104Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7067520Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7067914Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7068313Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7068667Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.7069069Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7069470Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7069849Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7070233Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7070585Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.7070985Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7071393Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7071773Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7072169Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7072511Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.7072931Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7073350Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7073728Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7074108Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7074448Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:04:05.7074868Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7075265Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7075651Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7076029Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7076373Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:04:05.7076779Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7077171Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7077557Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7077929Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7078296Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:04:05.7078706Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:04:05.7079105Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:04:05.7079513Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:04:05.7079896Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:04:05.7080133Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T21:04:05.7080349Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T21:04:05.7080576Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T21:04:05.7080803Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T21:04:05.7081028Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T21:04:05.7081238Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T21:04:05.7081460Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T21:04:05.7081673Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T21:04:05.7081887Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T21:04:05.7082103Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T21:04:05.7082320Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T21:04:05.7082534Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T21:04:05.7082750Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T21:04:05.7082967Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T21:04:05.7083180Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T21:04:05.7083389Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T21:04:05.7083740Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:04:05.7084099Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:04:05.7084457Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:04:05.7084802Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:04:05.7085145Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:04:05.7085478Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:04:05.7085806Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:04:05.7086171Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:04:05.7086532Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:04:05.7086875Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:04:05.7087242Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:04:05.7087313Z 2025-03-04T21:04:05.7087602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7088336Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7088420Z 2025-03-04T21:04:05.7088706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7090440Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7090520Z 2025-03-04T21:04:05.7090813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:04:05.7090967Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:04:05.7091041Z 2025-03-04T21:04:05.7091452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:04:05.7091710Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:04:05.7091778Z 2025-03-04T21:04:05.7092048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7092576Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7092686Z 2025-03-04T21:04:05.7092958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7094859Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7095000Z 2025-03-04T21:04:05.7095301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7095455Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:04:05.7095522Z 2025-03-04T21:04:05.7095787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7096294Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7096371Z 2025-03-04T21:04:05.7096641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7098452Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7098534Z 2025-03-04T21:04:05.7098830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7098982Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:04:05.7099049Z 2025-03-04T21:04:05.7099328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7099852Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7099929Z 2025-03-04T21:04:05.7100203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7102001Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7102094Z 2025-03-04T21:04:05.7102349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7102866Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:04:05.7102942Z 2025-03-04T21:04:05.7103209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7105083Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7105167Z 2025-03-04T21:04:05.7105452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7105605Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:04:05.7105671Z 2025-03-04T21:04:05.7105959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7106125Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:04:05.7106212Z 2025-03-04T21:04:05.7106461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7106947Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7107011Z 2025-03-04T21:04:05.7107282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7109030Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7109120Z 2025-03-04T21:04:05.7109412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7109561Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:04:05.7109634Z 2025-03-04T21:04:05.7109887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7110384Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7110450Z 2025-03-04T21:04:05.7110724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7112523Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7112594Z 2025-03-04T21:04:05.7112910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7113070Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:04:05.7113143Z 2025-03-04T21:04:05.7113395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7113886Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7113953Z 2025-03-04T21:04:05.7114224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7116021Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7116089Z 2025-03-04T21:04:05.7116374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7116528Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:04:05.7116600Z 2025-03-04T21:04:05.7116878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7117034Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:04:05.7117098Z 2025-03-04T21:04:05.7117353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7117837Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7117903Z 2025-03-04T21:04:05.7118192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7119948Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7120038Z 2025-03-04T21:04:05.7120321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7120460Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:04:05.7120532Z 2025-03-04T21:04:05.7120777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7121281Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7121347Z 2025-03-04T21:04:05.7121614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7123362Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7123438Z 2025-03-04T21:04:05.7123725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7123862Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:04:05.7123932Z 2025-03-04T21:04:05.7124179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7124686Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7124752Z 2025-03-04T21:04:05.7125022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7126819Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7126904Z 2025-03-04T21:04:05.7127195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7127355Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:04:05.7127446Z 2025-03-04T21:04:05.7127733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7127895Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:04:05.7127961Z 2025-03-04T21:04:05.7128221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7128713Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7128789Z 2025-03-04T21:04:05.7129058Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7130830Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7130907Z 2025-03-04T21:04:05.7131193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7131363Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:04:05.7131428Z 2025-03-04T21:04:05.7131686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7132184Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7132268Z 2025-03-04T21:04:05.7132554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7134430Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7134540Z 2025-03-04T21:04:05.7134858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7135018Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:04:05.7135096Z 2025-03-04T21:04:05.7135365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7135891Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7135962Z 2025-03-04T21:04:05.7136240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7138025Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7138104Z 2025-03-04T21:04:05.7138395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7138901Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:04:05.7138977Z 2025-03-04T21:04:05.7139268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7141136Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7141232Z 2025-03-04T21:04:05.7141515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7141674Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:04:05.7141740Z 2025-03-04T21:04:05.7142034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7142188Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:04:05.7142262Z 2025-03-04T21:04:05.7142514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7143008Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7143078Z 2025-03-04T21:04:05.7143354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7145158Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7145228Z 2025-03-04T21:04:05.7145526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7145673Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:04:05.7145750Z 2025-03-04T21:04:05.7146024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7146550Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7146623Z 2025-03-04T21:04:05.7146883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7148634Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7148725Z 2025-03-04T21:04:05.7149002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7149149Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:04:05.7149215Z 2025-03-04T21:04:05.7149470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7149954Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7150024Z 2025-03-04T21:04:05.7150284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7152052Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7152128Z 2025-03-04T21:04:05.7152407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7152608Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:04:05.7152689Z 2025-03-04T21:04:05.7152976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7153126Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:04:05.7153197Z 2025-03-04T21:04:05.7153445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7153929Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7154010Z 2025-03-04T21:04:05.7154282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7156043Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7156110Z 2025-03-04T21:04:05.7156396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7156537Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:04:05.7156609Z 2025-03-04T21:04:05.7156859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7157348Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7157415Z 2025-03-04T21:04:05.7157682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7159464Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7159548Z 2025-03-04T21:04:05.7159843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7159984Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:04:05.7160056Z 2025-03-04T21:04:05.7160305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7160811Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7160902Z 2025-03-04T21:04:05.7161166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7162938Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7163018Z 2025-03-04T21:04:05.7163294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7163457Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:04:05.7163525Z 2025-03-04T21:04:05.7163814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7163967Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:04:05.7164042Z 2025-03-04T21:04:05.7164290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7164797Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7164863Z 2025-03-04T21:04:05.7165134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7166878Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7166968Z 2025-03-04T21:04:05.7167260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7167415Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:04:05.7167488Z 2025-03-04T21:04:05.7167732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7168217Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7168283Z 2025-03-04T21:04:05.7168548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7170306Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7170374Z 2025-03-04T21:04:05.7170661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7170800Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:04:05.7170873Z 2025-03-04T21:04:05.7171135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7171629Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7171695Z 2025-03-04T21:04:05.7171986Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7173751Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7173839Z 2025-03-04T21:04:05.7174130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7174354Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:04:05.7174450Z 2025-03-04T21:04:05.7174758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7174928Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:04:05.7174998Z 2025-03-04T21:04:05.7175273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7175797Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7175867Z 2025-03-04T21:04:05.7176164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7178026Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7178110Z 2025-03-04T21:04:05.7178424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7178571Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:04:05.7178648Z 2025-03-04T21:04:05.7178930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7179469Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7179538Z 2025-03-04T21:04:05.7179828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7181723Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7181818Z 2025-03-04T21:04:05.7182128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7182274Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:04:05.7182351Z 2025-03-04T21:04:05.7182617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7183141Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7183213Z 2025-03-04T21:04:05.7183501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7185412Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7185486Z 2025-03-04T21:04:05.7185762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7186300Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:04:05.7186402Z 2025-03-04T21:04:05.7186663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7188575Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7188704Z 2025-03-04T21:04:05.7188984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7189136Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:04:05.7189203Z 2025-03-04T21:04:05.7189501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7189653Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:04:05.7189733Z 2025-03-04T21:04:05.7189989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7190481Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7190557Z 2025-03-04T21:04:05.7190826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7192663Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7192741Z 2025-03-04T21:04:05.7193046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7193209Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:04:05.7193274Z 2025-03-04T21:04:05.7193530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7194005Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7194077Z 2025-03-04T21:04:05.7194339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7196072Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7196148Z 2025-03-04T21:04:05.7196426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7196566Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:04:05.7196631Z 2025-03-04T21:04:05.7196888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7197381Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7197454Z 2025-03-04T21:04:05.7197713Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7199506Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7199627Z 2025-03-04T21:04:05.7199908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7200062Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:04:05.7200130Z 2025-03-04T21:04:05.7200419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7200562Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:04:05.7200634Z 2025-03-04T21:04:05.7200885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7201395Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7201461Z 2025-03-04T21:04:05.7201733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7203467Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7203533Z 2025-03-04T21:04:05.7203818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7203951Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:04:05.7204026Z 2025-03-04T21:04:05.7204278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7204772Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7204849Z 2025-03-04T21:04:05.7205128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7206927Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7207018Z 2025-03-04T21:04:05.7207308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7207451Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:04:05.7207516Z 2025-03-04T21:04:05.7207794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7208284Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7208357Z 2025-03-04T21:04:05.7208625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7210424Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7210501Z 2025-03-04T21:04:05.7210782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7210937Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:04:05.7211006Z 2025-03-04T21:04:05.7211298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7211442Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:04:05.7211517Z 2025-03-04T21:04:05.7211785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7212268Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7212333Z 2025-03-04T21:04:05.7212624Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7214466Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7214569Z 2025-03-04T21:04:05.7214885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7215029Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:04:05.7215111Z 2025-03-04T21:04:05.7215393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7215943Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7216016Z 2025-03-04T21:04:05.7216306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7218114Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7218182Z 2025-03-04T21:04:05.7218474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7218625Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:04:05.7218698Z 2025-03-04T21:04:05.7218948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7219451Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7219543Z 2025-03-04T21:04:05.7219815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7221613Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7221706Z 2025-03-04T21:04:05.7221991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7222143Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:04:05.7222209Z 2025-03-04T21:04:05.7222499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7222644Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:04:05.7222716Z 2025-03-04T21:04:05.7222971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7223463Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7223529Z 2025-03-04T21:04:05.7223803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7225621Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7225701Z 2025-03-04T21:04:05.7225994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7226146Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:04:05.7226234Z 2025-03-04T21:04:05.7226494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7226984Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7227050Z 2025-03-04T21:04:05.7227326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7229105Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7229191Z 2025-03-04T21:04:05.7229486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7229622Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:04:05.7229698Z 2025-03-04T21:04:05.7229950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7230449Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7230515Z 2025-03-04T21:04:05.7230792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7232596Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7232668Z 2025-03-04T21:04:05.7232977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7233140Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:04:05.7233219Z 2025-03-04T21:04:05.7233511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7233663Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:04:05.7233730Z 2025-03-04T21:04:05.7233991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7234475Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7234561Z 2025-03-04T21:04:05.7234837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7236626Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7236704Z 2025-03-04T21:04:05.7236999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7237132Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:04:05.7237205Z 2025-03-04T21:04:05.7237457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7237952Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7238021Z 2025-03-04T21:04:05.7238293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7240118Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7240210Z 2025-03-04T21:04:05.7240504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7240638Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:04:05.7240712Z 2025-03-04T21:04:05.7240964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7241478Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7241546Z 2025-03-04T21:04:05.7241822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7243645Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7243713Z 2025-03-04T21:04:05.7244000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7244145Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:04:05.7244220Z 2025-03-04T21:04:05.7244508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7244663Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:04:05.7244728Z 2025-03-04T21:04:05.7244988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7245493Z x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7245569Z 2025-03-04T21:04:05.7245835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7247631Z x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7247739Z 2025-03-04T21:04:05.7248031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7248174Z out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:04:05.7248240Z 2025-03-04T21:04:05.7248504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7248988Z x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7249061Z 2025-03-04T21:04:05.7249334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7251121Z x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7251199Z 2025-03-04T21:04:05.7251495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7251632Z out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:04:05.7251706Z 2025-03-04T21:04:05.7251976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7252470Z x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7252535Z 2025-03-04T21:04:05.7252823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7254654Z x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7254753Z 2025-03-04T21:04:05.7255040Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7255189Z x_93 += out_51; out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T21:04:05.7255267Z 2025-03-04T21:04:05.7255566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7255722Z out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:04:05.7255798Z 2025-03-04T21:04:05.7256059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7256547Z x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7256622Z 2025-03-04T21:04:05.7256890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7258707Z x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7258784Z 2025-03-04T21:04:05.7259077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7259218Z out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T21:04:05.7259285Z 2025-03-04T21:04:05.7259578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7260081Z x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7260158Z 2025-03-04T21:04:05.7260427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7262217Z x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7262312Z 2025-03-04T21:04:05.7262599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7262741Z out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:04:05.7262808Z 2025-03-04T21:04:05.7263070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7263558Z x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7263633Z 2025-03-04T21:04:05.7263907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7265705Z x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7265785Z 2025-03-04T21:04:05.7266075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7266240Z x_99 += out_55; out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T21:04:05.7266326Z 2025-03-04T21:04:05.7266605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7266752Z out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:04:05.7266818Z 2025-03-04T21:04:05.7267069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7267545Z x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7267629Z 2025-03-04T21:04:05.7267893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7269641Z x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7269715Z 2025-03-04T21:04:05.7269994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7270141Z out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T21:04:05.7270206Z 2025-03-04T21:04:05.7270460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7270933Z x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7271007Z 2025-03-04T21:04:05.7271267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7273082Z x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7273172Z 2025-03-04T21:04:05.7273469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7273615Z out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:04:05.7273679Z 2025-03-04T21:04:05.7273932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7274406Z x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7274495Z 2025-03-04T21:04:05.7274756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7276516Z x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7276592Z 2025-03-04T21:04:05.7276866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7277027Z x_105 += out_59; out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T21:04:05.7277091Z 2025-03-04T21:04:05.7277377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7277519Z out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:04:05.7277596Z 2025-03-04T21:04:05.7277843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7278336Z x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7278407Z 2025-03-04T21:04:05.7278668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7280426Z x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7282396Z 2025-03-04T21:04:05.7282785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7283293Z out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T21:04:05.7283565Z 2025-03-04T21:04:05.7283916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7284732Z x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7285355Z 2025-03-04T21:04:05.7285725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7287846Z x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7289893Z 2025-03-04T21:04:05.7290287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7290776Z out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T21:04:05.7291057Z 2025-03-04T21:04:05.7291420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7292322Z x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7292958Z 2025-03-04T21:04:05.7293338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7296029Z x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7298082Z 2025-03-04T21:04:05.7298471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7298977Z x_111 += out_63; out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T21:04:05.7299259Z 2025-03-04T21:04:05.7299645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7300143Z out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T21:04:05.7300426Z 2025-03-04T21:04:05.7300770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7301586Z x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7302203Z 2025-03-04T21:04:05.7302563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7304736Z x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7306694Z 2025-03-04T21:04:05.7307084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7307555Z out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T21:04:05.7307819Z 2025-03-04T21:04:05.7308157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7308983Z x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7309611Z 2025-03-04T21:04:05.7309970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7312090Z x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7313974Z 2025-03-04T21:04:05.7314344Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7314817Z out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T21:04:05.7315075Z 2025-03-04T21:04:05.7315410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7316201Z x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7316807Z 2025-03-04T21:04:05.7317158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7319262Z x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7321148Z 2025-03-04T21:04:05.7321512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7321998Z x_117 += out_67; out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T21:04:05.7322286Z 2025-03-04T21:04:05.7322680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7323171Z out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T21:04:05.7323447Z 2025-03-04T21:04:05.7323798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7324616Z x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7325229Z 2025-03-04T21:04:05.7325591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7327758Z x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7329656Z 2025-03-04T21:04:05.7330042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7330532Z out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T21:04:05.7330806Z 2025-03-04T21:04:05.7331155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7331966Z x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7332580Z 2025-03-04T21:04:05.7332946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7335264Z x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7337400Z 2025-03-04T21:04:05.7337787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7338283Z out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T21:04:05.7338556Z 2025-03-04T21:04:05.7338900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7339768Z x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7340467Z 2025-03-04T21:04:05.7340852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7343113Z x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7345150Z 2025-03-04T21:04:05.7345546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7346070Z x_123 += out_71; out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T21:04:05.7346361Z 2025-03-04T21:04:05.7346758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7347273Z out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T21:04:05.7347556Z 2025-03-04T21:04:05.7347918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7348793Z x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7349435Z 2025-03-04T21:04:05.7349819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7352076Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7354020Z 2025-03-04T21:04:05.7354408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7354927Z out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T21:04:05.7355201Z 2025-03-04T21:04:05.7355548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7356367Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7356988Z 2025-03-04T21:04:05.7357352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7359509Z x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7361415Z 2025-03-04T21:04:05.7361798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7362290Z out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T21:04:05.7362557Z 2025-03-04T21:04:05.7362902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7363750Z x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7364352Z 2025-03-04T21:04:05.7364704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7366837Z x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7368735Z 2025-03-04T21:04:05.7369099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7369582Z x_129 += out_75; out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T21:04:05.7369850Z 2025-03-04T21:04:05.7370215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7370698Z out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T21:04:05.7370962Z 2025-03-04T21:04:05.7371288Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7372083Z x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7372692Z 2025-03-04T21:04:05.7373049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7375309Z x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7377275Z 2025-03-04T21:04:05.7377664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7378173Z out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T21:04:05.7378452Z 2025-03-04T21:04:05.7378805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7379658Z x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7380289Z 2025-03-04T21:04:05.7380658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7382795Z x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7384747Z 2025-03-04T21:04:05.7385126Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7385617Z out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T21:04:05.7385880Z 2025-03-04T21:04:05.7386223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7387041Z x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7387659Z 2025-03-04T21:04:05.7388018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7390319Z x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7392223Z 2025-03-04T21:04:05.7392596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7393089Z x_135 += out_79; out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T21:04:05.7393366Z 2025-03-04T21:04:05.7393769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7394295Z out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T21:04:05.7394557Z 2025-03-04T21:04:05.7394896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7395689Z x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7396282Z 2025-03-04T21:04:05.7396641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7398786Z x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7400683Z 2025-03-04T21:04:05.7401050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7401528Z out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T21:04:05.7401786Z 2025-03-04T21:04:05.7402120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7402916Z x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7403507Z 2025-03-04T21:04:05.7403865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7405970Z x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7407831Z 2025-03-04T21:04:05.7408201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7408678Z out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T21:04:05.7408942Z 2025-03-04T21:04:05.7409284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7410091Z x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7410717Z 2025-03-04T21:04:05.7411073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7413198Z x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7415207Z 2025-03-04T21:04:05.7415607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7416122Z x_141 += out_83; out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T21:04:05.7416396Z 2025-03-04T21:04:05.7416773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7417265Z out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T21:04:05.7417538Z 2025-03-04T21:04:05.7417884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7418715Z x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7419325Z 2025-03-04T21:04:05.7419680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7421817Z x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7423727Z 2025-03-04T21:04:05.7424111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7424635Z out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T21:04:05.7424901Z 2025-03-04T21:04:05.7425240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7426048Z x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7426656Z 2025-03-04T21:04:05.7427012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7429141Z x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7431041Z 2025-03-04T21:04:05.7431417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7431912Z out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T21:04:05.7432174Z 2025-03-04T21:04:05.7432520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7433368Z x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7433987Z 2025-03-04T21:04:05.7434349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7436504Z x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7438458Z 2025-03-04T21:04:05.7438831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7439327Z x_147 += out_87; out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T21:04:05.7439606Z 2025-03-04T21:04:05.7439987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7440473Z out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T21:04:05.7440738Z 2025-03-04T21:04:05.7441078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7441880Z x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7442490Z 2025-03-04T21:04:05.7442845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7444998Z x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7446917Z 2025-03-04T21:04:05.7447305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7447781Z out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T21:04:05.7448037Z 2025-03-04T21:04:05.7448368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7449191Z x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7449826Z 2025-03-04T21:04:05.7450188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7452318Z x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7454311Z 2025-03-04T21:04:05.7454691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7455204Z out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T21:04:05.7455472Z 2025-03-04T21:04:05.7455831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7456702Z x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7457357Z 2025-03-04T21:04:05.7457715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7459891Z x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7461787Z 2025-03-04T21:04:05.7462160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7462652Z x_153 += out_91; out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T21:04:05.7462924Z 2025-03-04T21:04:05.7463331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7463835Z out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T21:04:05.7464100Z 2025-03-04T21:04:05.7464445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7465243Z x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7465847Z 2025-03-04T21:04:05.7466204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7468370Z x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7470281Z 2025-03-04T21:04:05.7470666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7471166Z out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T21:04:05.7471436Z 2025-03-04T21:04:05.7471786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7472605Z x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7473218Z 2025-03-04T21:04:05.7473582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7475740Z x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7477641Z 2025-03-04T21:04:05.7478017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7478504Z out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T21:04:05.7478769Z 2025-03-04T21:04:05.7479112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7479914Z x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7480546Z 2025-03-04T21:04:05.7480896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7482971Z x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7484857Z 2025-03-04T21:04:05.7485213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7485697Z x_159 += out_95; out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T21:04:05.7485970Z 2025-03-04T21:04:05.7486343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7486827Z out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T21:04:05.7487096Z 2025-03-04T21:04:05.7487439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7488481Z x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7489093Z 2025-03-04T21:04:05.7489450Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7491617Z x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7493518Z 2025-03-04T21:04:05.7493907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7494488Z out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T21:04:05.7494763Z 2025-03-04T21:04:05.7495115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7495974Z x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7496616Z 2025-03-04T21:04:05.7496973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7499197Z x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7501196Z 2025-03-04T21:04:05.7501600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7502120Z out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T21:04:05.7502406Z 2025-03-04T21:04:05.7502768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7503657Z x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7504310Z 2025-03-04T21:04:05.7504692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7506939Z x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7508887Z 2025-03-04T21:04:05.7509258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7509753Z x_165 += out_99; out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T21:04:05.7510032Z 2025-03-04T21:04:05.7510398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7510896Z out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T21:04:05.7511162Z 2025-03-04T21:04:05.7511505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7512310Z x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7512919Z 2025-03-04T21:04:05.7513270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7515400Z x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7517274Z 2025-03-04T21:04:05.7517670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7518159Z out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T21:04:05.7518424Z 2025-03-04T21:04:05.7518766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7519594Z x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7520224Z 2025-03-04T21:04:05.7520581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7522695Z x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7524608Z 2025-03-04T21:04:05.7524983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7525469Z out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T21:04:05.7525734Z 2025-03-04T21:04:05.7526072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7526888Z x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7527508Z 2025-03-04T21:04:05.7527865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7530031Z x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7531929Z 2025-03-04T21:04:05.7532300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7532803Z x_171 += out_103; out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T21:04:05.7533088Z 2025-03-04T21:04:05.7533482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7533996Z out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T21:04:05.7534323Z 2025-03-04T21:04:05.7534684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7535556Z x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7536192Z 2025-03-04T21:04:05.7536549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7538688Z x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7540601Z 2025-03-04T21:04:05.7540972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7541451Z out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T21:04:05.7541718Z 2025-03-04T21:04:05.7542060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7542870Z x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7543477Z 2025-03-04T21:04:05.7543835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7545994Z x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7547914Z 2025-03-04T21:04:05.7548290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7548774Z out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T21:04:05.7549040Z 2025-03-04T21:04:05.7549380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7550194Z x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7550833Z 2025-03-04T21:04:05.7551188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7553318Z x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7555201Z 2025-03-04T21:04:05.7555564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7556050Z x_177 += out_107; out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T21:04:05.7556328Z 2025-03-04T21:04:05.7556693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7557173Z out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T21:04:05.7557438Z 2025-03-04T21:04:05.7557772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7558625Z x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7559248Z 2025-03-04T21:04:05.7559608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7561774Z x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7563729Z 2025-03-04T21:04:05.7564107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7564614Z out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T21:04:05.7564883Z 2025-03-04T21:04:05.7565217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7566040Z x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7566636Z 2025-03-04T21:04:05.7566987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7569062Z x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7570925Z 2025-03-04T21:04:05.7571303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7571791Z out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T21:04:05.7572054Z 2025-03-04T21:04:05.7572399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7573235Z x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7573853Z 2025-03-04T21:04:05.7574257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7576518Z x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7578479Z 2025-03-04T21:04:05.7578846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7579345Z x_183 += out_111; out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T21:04:05.7579627Z 2025-03-04T21:04:05.7580003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7580491Z out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T21:04:05.7580762Z 2025-03-04T21:04:05.7581102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7581907Z x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7582514Z 2025-03-04T21:04:05.7582869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7585233Z x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7587127Z 2025-03-04T21:04:05.7587531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7588027Z out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T21:04:05.7588450Z 2025-03-04T21:04:05.7588789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7589616Z x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7590235Z 2025-03-04T21:04:05.7590588Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7592668Z x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7594542Z 2025-03-04T21:04:05.7594917Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7595398Z out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T21:04:05.7595663Z 2025-03-04T21:04:05.7596004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7596816Z x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7597435Z 2025-03-04T21:04:05.7597796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7599925Z x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7601833Z 2025-03-04T21:04:05.7602204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7602713Z x_189 += out_115; out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T21:04:05.7602981Z 2025-03-04T21:04:05.7603363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7603861Z out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T21:04:05.7604125Z 2025-03-04T21:04:05.7604460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7605236Z x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7605824Z 2025-03-04T21:04:05.7606174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7608248Z x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7610073Z 2025-03-04T21:04:05.7610441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7610918Z out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191); x_191 = None 2025-03-04T21:04:05.7611175Z 2025-03-04T21:04:05.7611508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7612292Z x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7612887Z 2025-03-04T21:04:05.7613232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7615500Z x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7617402Z 2025-03-04T21:04:05.7617781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7618268Z out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193); x_193 = None 2025-03-04T21:04:05.7618530Z 2025-03-04T21:04:05.7618878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7619693Z x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7620327Z 2025-03-04T21:04:05.7620691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7622819Z x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7624731Z 2025-03-04T21:04:05.7625072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7625882Z x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:04:05.7626497Z 2025-03-04T21:04:05.7626852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7629093Z x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7631220Z 2025-03-04T21:04:05.7631590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7632085Z x_195 += x_197; out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195; x_195 = x_197 = None 2025-03-04T21:04:05.7632354Z 2025-03-04T21:04:05.7632730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7633222Z out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122); out_122 = None 2025-03-04T21:04:05.7633493Z 2025-03-04T21:04:05.7633835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7634624Z x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7635244Z 2025-03-04T21:04:05.7635598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7637712Z x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7639592Z 2025-03-04T21:04:05.7639964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7640446Z out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199); x_199 = None 2025-03-04T21:04:05.7640705Z 2025-03-04T21:04:05.7641047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7641850Z x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7642456Z 2025-03-04T21:04:05.7642834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7644978Z x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7646853Z 2025-03-04T21:04:05.7647228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7647719Z out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201); x_201 = None 2025-03-04T21:04:05.7647976Z 2025-03-04T21:04:05.7648331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7649134Z x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7649748Z 2025-03-04T21:04:05.7650105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7652236Z x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7654202Z 2025-03-04T21:04:05.7654625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7655181Z x_203 += out_123; out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203; x_203 = out_123 = None 2025-03-04T21:04:05.7655499Z 2025-03-04T21:04:05.7655895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7656413Z out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126); out_126 = None 2025-03-04T21:04:05.7656698Z 2025-03-04T21:04:05.7657097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7657922Z x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:04:05.7658524Z 2025-03-04T21:04:05.7658904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7661054Z x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7662937Z 2025-03-04T21:04:05.7663313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7663796Z out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205); x_205 = None 2025-03-04T21:04:05.7664064Z 2025-03-04T21:04:05.7664422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7665225Z x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:04:05.7665833Z 2025-03-04T21:04:05.7666188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7668290Z x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7670175Z 2025-03-04T21:04:05.7670549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7671066Z out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207); x_207 = None 2025-03-04T21:04:05.7671332Z 2025-03-04T21:04:05.7671676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7672499Z x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:04:05.7672584Z 2025-03-04T21:04:05.7672862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:04:05.7674646Z x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:04:05.7674740Z 2025-03-04T21:04:05.7675031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:04:05.7675196Z x_209 += out_127; out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209; x_209 = out_127 = None 2025-03-04T21:04:05.7675270Z 2025-03-04T21:04:05.7675558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:04:05.7675712Z out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130); out_130 = None 2025-03-04T21:04:05.7675781Z 2025-03-04T21:04:05.7676046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7676618Z x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T21:04:05.7676694Z 2025-03-04T21:04:05.7676950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7677505Z x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T21:04:05.7677573Z 2025-03-04T21:04:05.7678001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:04:05.7678294Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest'); x_210 = None 2025-03-04T21:04:05.7678361Z 2025-03-04T21:04:05.7678622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7679260Z x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T21:04:05.7679350Z 2025-03-04T21:04:05.7679702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:04:05.7679907Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features; x_212 = top_down_features = None 2025-03-04T21:04:05.7679972Z 2025-03-04T21:04:05.7680230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7680804Z x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T21:04:05.7680899Z 2025-03-04T21:04:05.7681303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:04:05.7681637Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T21:04:05.7681705Z 2025-03-04T21:04:05.7681965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7682552Z x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T21:04:05.7682620Z 2025-03-04T21:04:05.7682975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:04:05.7683188Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1; x_214 = top_down_features_1 = None 2025-03-04T21:04:05.7683261Z 2025-03-04T21:04:05.7683512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7684097Z x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T21:04:05.7684166Z 2025-03-04T21:04:05.7684595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:04:05.7684924Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T21:04:05.7684998Z 2025-03-04T21:04:05.7685252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7685857Z x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T21:04:05.7685949Z 2025-03-04T21:04:05.7686300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:04:05.7686522Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2; x_216 = top_down_features_2 = None 2025-03-04T21:04:05.7686589Z 2025-03-04T21:04:05.7686849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7687467Z x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T21:04:05.7687559Z 2025-03-04T21:04:05.7687926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T21:04:05.7688270Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T21:04:05.7688345Z 2025-03-04T21:04:05.7688799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.7688963Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:04:05.7689041Z 2025-03-04T21:04:05.7689345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.7689501Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:04:05.7689568Z 2025-03-04T21:04:05.7690023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.7690189Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:04:05.7690256Z 2025-03-04T21:04:05.7690570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.7690718Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:04:05.7690795Z 2025-03-04T21:04:05.7691218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:05.7691409Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:04:05.7691513Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:04:05.7691645Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:04:05.7691710Z 2025-03-04T21:04:05.7692054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:05.7692212Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:04:05.7692313Z 2025-03-04T21:04:05.7692645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:05.7692777Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:04:05.7692846Z 2025-03-04T21:04:05.7693243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:05.7693464Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:04:05.7693538Z 2025-03-04T21:04:05.7693965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:05.7694138Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:04:05.7694693Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:04:05.7694835Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:04:05.7694964Z x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:04:05.7695043Z 2025-03-04T21:04:05.7695511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.7695672Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:04:05.7695738Z 2025-03-04T21:04:05.7696044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.7696190Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:04:05.7696267Z 2025-03-04T21:04:05.7696702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.7696860Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:04:05.7696936Z 2025-03-04T21:04:05.7697232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.7697380Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:04:05.7697447Z 2025-03-04T21:04:05.7697851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:05.7698060Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:04:05.7698182Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:04:05.7698313Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:04:05.7698387Z 2025-03-04T21:04:05.7698738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:05.7698894Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:04:05.7698963Z 2025-03-04T21:04:05.7699303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:05.7699436Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:04:05.7699510Z 2025-03-04T21:04:05.7699907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:05.7700138Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:04:05.7700205Z 2025-03-04T21:04:05.7700673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:05.7700808Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:04:05.7701244Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:04:05.7701372Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:04:05.7701500Z x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:04:05.7701565Z 2025-03-04T21:04:05.7702006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.7702157Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:04:05.7702231Z 2025-03-04T21:04:05.7702526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.7702676Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:04:05.7702744Z 2025-03-04T21:04:05.7703182Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.7703334Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:04:05.7703401Z 2025-03-04T21:04:05.7703706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.7703844Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:04:05.7703917Z 2025-03-04T21:04:05.7704306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:05.7704511Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:04:05.7704613Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:04:05.7704746Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:04:05.7704811Z 2025-03-04T21:04:05.7705162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:05.7705308Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:04:05.7705381Z 2025-03-04T21:04:05.7705705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:05.7705836Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:04:05.7705902Z 2025-03-04T21:04:05.7706291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:05.7706505Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:04:05.7706597Z 2025-03-04T21:04:05.7707011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:05.7707152Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:04:05.7707578Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:04:05.7707713Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:04:05.7707830Z x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T21:04:05.7707906Z 2025-03-04T21:04:05.7708341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.7708499Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:04:05.7708565Z 2025-03-04T21:04:05.7708870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.7709008Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:04:05.7709083Z 2025-03-04T21:04:05.7709510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.7709662Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:04:05.7709737Z 2025-03-04T21:04:05.7710034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.7710178Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:04:05.7710243Z 2025-03-04T21:04:05.7710637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:05.7710829Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:04:05.7710938Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:04:05.7711059Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:04:05.7711132Z 2025-03-04T21:04:05.7711476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:05.7711623Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:04:05.7711688Z 2025-03-04T21:04:05.7712024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:05.7712144Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:04:05.7712216Z 2025-03-04T21:04:05.7712585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:05.7712799Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:04:05.7712879Z 2025-03-04T21:04:05.7713287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:05.7713412Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:04:05.7713830Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:04:05.7713950Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:04:05.7714073Z x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T21:04:05.7714138Z 2025-03-04T21:04:05.7714565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.7714709Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:04:05.7714785Z 2025-03-04T21:04:05.7715075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.7715213Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:04:05.7715277Z 2025-03-04T21:04:05.7715707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:05.7715856Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:04:05.7715921Z 2025-03-04T21:04:05.7716214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.7716345Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:04:05.7716416Z 2025-03-04T21:04:05.7716793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:05.7716986Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:04:05.7717083Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:04:05.7717207Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:04:05.7717271Z 2025-03-04T21:04:05.7717614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:05.7717762Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:04:05.7717833Z 2025-03-04T21:04:05.7718151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:05.7718279Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:04:05.7718342Z 2025-03-04T21:04:05.7718719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:05.7718926Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:04:05.7719047Z 2025-03-04T21:04:05.7719454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:05.7719585Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:04:05.7719994Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:04:05.7720119Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:04:05.7720232Z x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T21:04:05.7720306Z 2025-03-04T21:04:05.7720603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:04:05.7720741Z tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32); x_218 = None 2025-03-04T21:04:05.7720873Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32); x_219 = None 2025-03-04T21:04:05.7721008Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32); x_220 = None 2025-03-04T21:04:05.7721130Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32); x_221 = None 2025-03-04T21:04:05.7721258Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32); x_222 = None 2025-03-04T21:04:05.7721324Z 2025-03-04T21:04:05.7721592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7722114Z x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_217 = None 2025-03-04T21:04:05.7722190Z 2025-03-04T21:04:05.7722496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:05.7722701Z x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False); x_223 = None 2025-03-04T21:04:05.7722776Z 2025-03-04T21:04:05.7723165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:05.7723711Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:05.7723795Z 2025-03-04T21:04:05.7724168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:05.7724698Z x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_224 = None 2025-03-04T21:04:05.7724773Z 2025-03-04T21:04:05.7725030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7725537Z x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_215 = None 2025-03-04T21:04:05.7725604Z 2025-03-04T21:04:05.7725891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:05.7726090Z x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False); x_225 = None 2025-03-04T21:04:05.7726162Z 2025-03-04T21:04:05.7726547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:05.7727064Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:05.7727141Z 2025-03-04T21:04:05.7727500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:05.7728026Z x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_226 = None 2025-03-04T21:04:05.7728091Z 2025-03-04T21:04:05.7728357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7728838Z x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_213 = None 2025-03-04T21:04:05.7728914Z 2025-03-04T21:04:05.7729207Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:05.7729406Z x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False); x_227 = None 2025-03-04T21:04:05.7729472Z 2025-03-04T21:04:05.7729856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:05.7730382Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:05.7730462Z 2025-03-04T21:04:05.7730827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:05.7731336Z x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_228 = None 2025-03-04T21:04:05.7731410Z 2025-03-04T21:04:05.7731663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7732156Z x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_211 = None 2025-03-04T21:04:05.7732223Z 2025-03-04T21:04:05.7732504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:05.7732688Z x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False); x_229 = None 2025-03-04T21:04:05.7732761Z 2025-03-04T21:04:05.7733135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:05.7733641Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:05.7733713Z 2025-03-04T21:04:05.7734069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:05.7734646Z x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_230 = None 2025-03-04T21:04:05.7734716Z 2025-03-04T21:04:05.7734988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:05.7735806Z x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:04:05.7735885Z 2025-03-04T21:04:05.7736180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:05.7736369Z x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False); x_231 = None 2025-03-04T21:04:05.7736436Z 2025-03-04T21:04:05.7736836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:05.7737720Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:04:05.7737788Z 2025-03-04T21:04:05.7738152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:05.7738975Z x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:04:05.7739067Z 2025-03-04T21:04:05.7739415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:04:05.7739594Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:04:05.7739740Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:04:05.7739916Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:04:05.7740067Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:04:05.7740235Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:04:05.7740376Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:04:05.7740536Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:04:05.7740674Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:04:05.7740828Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:04:05.7740969Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:04:05.7741036Z 2025-03-04T21:04:05.7741471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:04:05.7741657Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304); x_233 = None 2025-03-04T21:04:05.7741866Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:04:05.7742051Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:04:05.7742225Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152); x_234 = None 2025-03-04T21:04:05.7742404Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:04:05.7742605Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:04:05.7742774Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76); x_235 = None 2025-03-04T21:04:05.7742949Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:04:05.7743121Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:04:05.7743277Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38); x_236 = None 2025-03-04T21:04:05.7743440Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:04:05.7743616Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:04:05.7743758Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19); x_237 = None 2025-03-04T21:04:05.7743944Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:04:05.7744111Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:04:05.7744189Z 2025-03-04T21:04:05.7744605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:05.7744819Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:04:05.7744887Z 2025-03-04T21:04:05.7745352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:05.7745520Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:04:05.7745671Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:04:05.7745820Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:04:05.7745887Z 2025-03-04T21:04:05.7746272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:05.7746445Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:04:05.7746518Z 2025-03-04T21:04:05.7746837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:05.7746989Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:04:05.7747057Z 2025-03-04T21:04:05.7747384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:05.7747517Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:04:05.7747680Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:05.7747837Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:04:05.7747911Z 2025-03-04T21:04:05.7748235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:05.7748370Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:04:05.7748510Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:04:05.7748694Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:04:05.7748760Z 2025-03-04T21:04:05.7749086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:05.7749211Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:05.7749311Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:04:05.7749442Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:04:05.7749516Z 2025-03-04T21:04:05.7749833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:05.7750012Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:04:05.7750106Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:04:05.7750248Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:04:05.7750315Z 2025-03-04T21:04:05.7750663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:05.7750821Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:05.7750945Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:04:05.7751012Z 2025-03-04T21:04:05.7751318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:05.7751476Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:05.7751601Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:04:05.7751667Z 2025-03-04T21:04:05.7751969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:05.7752131Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:05.7752244Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:04:05.7752317Z 2025-03-04T21:04:05.7752616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:05.7752806Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:04:05.7752924Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:04:05.7752997Z 2025-03-04T21:04:05.7753336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:05.7753508Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:04:05.7753575Z 2025-03-04T21:04:05.7753916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:05.7754054Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:04:05.7754126Z 2025-03-04T21:04:05.7754490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:05.7754652Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:04:05.7754779Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:04:05.7754945Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:04:05.7755086Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:04:05.7755160Z 2025-03-04T21:04:05.7755509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:05.7755657Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:04:05.7755783Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:04:05.7755964Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:04:05.7756103Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:04:05.7756180Z 2025-03-04T21:04:05.7756524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:05.7756656Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:04:05.7756821Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:04:05.7756967Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:04:05.7757033Z 2025-03-04T21:04:05.7757386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:05.7757509Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:04:05.7757687Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:04:05.7757823Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:04:05.7757899Z 2025-03-04T21:04:05.7758220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:05.7758328Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:04:05.7758449Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:04:05.7758526Z 2025-03-04T21:04:05.7758841Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:05.7758950Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:04:05.7759067Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:04:05.7759142Z 2025-03-04T21:04:05.7759476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:05.7759603Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:04:05.7759735Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:04:05.7759808Z 2025-03-04T21:04:05.7760115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:05.7760258Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:04:05.7760407Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:04:05.7760472Z 2025-03-04T21:04:05.7760825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:05.7761010Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:04:05.7761084Z 2025-03-04T21:04:05.7761421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:05.7761594Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:04:05.7761659Z 2025-03-04T21:04:05.7762076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:05.7762257Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:04:05.7762331Z 2025-03-04T21:04:05.7762735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:05.7762957Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:04:05.7763023Z 2025-03-04T21:04:05.7763471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:05.7763632Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:04:05.7763794Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:04:05.7763934Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:04:05.7764010Z 2025-03-04T21:04:05.7764388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:05.7764572Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:04:05.7764637Z 2025-03-04T21:04:05.7764957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:05.7765108Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:04:05.7765184Z 2025-03-04T21:04:05.7765499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:05.7765644Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:04:05.7765804Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:04:05.7765968Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:04:05.7766034Z 2025-03-04T21:04:05.7766361Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:05.7766489Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:04:05.7766638Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:04:05.7766809Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:04:05.7766886Z 2025-03-04T21:04:05.7767198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:05.7767334Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:04:05.7767437Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:04:05.7767572Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:04:05.7767638Z 2025-03-04T21:04:05.7767958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:05.7768136Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:04:05.7768235Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:04:05.7768372Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:04:05.7768438Z 2025-03-04T21:04:05.7768752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:05.7768909Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:05.7769033Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:04:05.7769098Z 2025-03-04T21:04:05.7769409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:05.7769566Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:05.7769687Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:04:05.7769752Z 2025-03-04T21:04:05.7770057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:05.7770215Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:05.7770334Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:04:05.7770400Z 2025-03-04T21:04:05.7770714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:05.7770904Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:04:05.7771029Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:04:05.7771095Z 2025-03-04T21:04:05.7771444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:05.7771610Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:04:05.7771684Z 2025-03-04T21:04:05.7772018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:05.7772165Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:04:05.7772230Z 2025-03-04T21:04:05.7772599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:05.7772757Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:04:05.7772894Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:04:05.7773056Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:04:05.7773210Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:04:05.7773277Z 2025-03-04T21:04:05.7773632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:05.7773780Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:04:05.7773923Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:04:05.7774095Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:04:05.7774321Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:04:05.7774408Z 2025-03-04T21:04:05.7774764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:05.7774900Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:04:05.7775076Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:04:05.7775236Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:04:05.7775308Z 2025-03-04T21:04:05.7775673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:05.7775800Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:04:05.7776001Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:04:05.7776146Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:04:05.7776224Z 2025-03-04T21:04:05.7776543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:05.7776656Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:04:05.7776784Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:04:05.7776861Z 2025-03-04T21:04:05.7777179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:05.7777291Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:04:05.7777417Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:04:05.7777497Z 2025-03-04T21:04:05.7777846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:05.7777986Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:04:05.7778131Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:04:05.7778210Z 2025-03-04T21:04:05.7778556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:05.7778709Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:04:05.7778851Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:04:05.7778929Z 2025-03-04T21:04:05.7779299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:05.7779511Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:04:05.7779581Z 2025-03-04T21:04:05.7779939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:05.7780117Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:04:05.7780215Z 2025-03-04T21:04:05.7780627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:05.7780825Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:04:05.7780895Z 2025-03-04T21:04:05.7781328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:05.7781560Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:04:05.7781629Z 2025-03-04T21:04:05.7782113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:05.7782281Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:04:05.7782451Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:04:05.7782601Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:04:05.7782678Z 2025-03-04T21:04:05.7783074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:05.7783262Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:04:05.7783332Z 2025-03-04T21:04:05.7783677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:05.7783840Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:04:05.7783916Z 2025-03-04T21:04:05.7784262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:05.7784408Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:04:05.7784542Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:04:05.7784708Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:04:05.7784776Z 2025-03-04T21:04:05.7785115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:05.7785262Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:04:05.7785413Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:04:05.7785572Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:04:05.7785648Z 2025-03-04T21:04:05.7785979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:05.7786116Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:04:05.7786215Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:04:05.7786363Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:04:05.7786434Z 2025-03-04T21:04:05.7786772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:05.7786954Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:04:05.7787074Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:04:05.7787208Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:04:05.7787284Z 2025-03-04T21:04:05.7787588Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:05.7787751Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:05.7787868Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:04:05.7787944Z 2025-03-04T21:04:05.7788358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:05.7788528Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:05.7788648Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:04:05.7788723Z 2025-03-04T21:04:05.7789031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:05.7789182Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:05.7789302Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:04:05.7789370Z 2025-03-04T21:04:05.7789680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:05.7789868Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:04:05.7789988Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:04:05.7790056Z 2025-03-04T21:04:05.7790443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:05.7790589Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:04:05.7790663Z 2025-03-04T21:04:05.7790994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:05.7791138Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:04:05.7791205Z 2025-03-04T21:04:05.7791580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:05.7791735Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:04:05.7791871Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:04:05.7792031Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:04:05.7792178Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:04:05.7792244Z 2025-03-04T21:04:05.7792598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:05.7792758Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:04:05.7792893Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:04:05.7793048Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:04:05.7793196Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:04:05.7793263Z 2025-03-04T21:04:05.7793609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:05.7793730Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:04:05.7793902Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:04:05.7794041Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:04:05.7794118Z 2025-03-04T21:04:05.7794456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:05.7794580Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:04:05.7794750Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:04:05.7794892Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:04:05.7794958Z 2025-03-04T21:04:05.7795283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:05.7795384Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:04:05.7795512Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:04:05.7795580Z 2025-03-04T21:04:05.7795899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:05.7796002Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:04:05.7796120Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:04:05.7796209Z 2025-03-04T21:04:05.7796518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:05.7796642Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:04:05.7796776Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:04:05.7796848Z 2025-03-04T21:04:05.7797164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:05.7797304Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:04:05.7797435Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:04:05.7797509Z 2025-03-04T21:04:05.7797858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:05.7798058Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:04:05.7798123Z 2025-03-04T21:04:05.7798462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:05.7798641Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:04:05.7798715Z 2025-03-04T21:04:05.7799095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:05.7799280Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:04:05.7799345Z 2025-03-04T21:04:05.7799751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:05.7799957Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:04:05.7800029Z 2025-03-04T21:04:05.7800463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:05.7800623Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:04:05.7800774Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:04:05.7800919Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:04:05.7800985Z 2025-03-04T21:04:05.7801365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:05.7801533Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:04:05.7801608Z 2025-03-04T21:04:05.7801921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:05.7802077Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:04:05.7802142Z 2025-03-04T21:04:05.7802478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:05.7802609Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:04:05.7802743Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:04:05.7802892Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:04:05.7802968Z 2025-03-04T21:04:05.7803298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:05.7803444Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:04:05.7803574Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:04:05.7803726Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:04:05.7803802Z 2025-03-04T21:04:05.7804112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:05.7804243Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:04:05.7804337Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:04:05.7804477Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:04:05.7804559Z 2025-03-04T21:04:05.7804880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:05.7805032Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:04:05.7805152Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:04:05.7805287Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:04:05.7805362Z 2025-03-04T21:04:05.7805668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:05.7805835Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:05.7805951Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:04:05.7806026Z 2025-03-04T21:04:05.7806330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:05.7806492Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:05.7806607Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:04:05.7806679Z 2025-03-04T21:04:05.7806978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:05.7807137Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:05.7807250Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:04:05.7807325Z 2025-03-04T21:04:05.7807630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:05.7807830Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:04:05.7807942Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:04:05.7808018Z 2025-03-04T21:04:05.7808369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:05.7808520Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:04:05.7808586Z 2025-03-04T21:04:05.7808923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:05.7809076Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:04:05.7809175Z 2025-03-04T21:04:05.7809521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:05.7809664Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:04:05.7809799Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:04:05.7809955Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:04:05.7810106Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:04:05.7810174Z 2025-03-04T21:04:05.7810531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:05.7810688Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:04:05.7810823Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:04:05.7810974Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:04:05.7811120Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:04:05.7811187Z 2025-03-04T21:04:05.7811528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:05.7811643Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:04:05.7811811Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:04:05.7811950Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:04:05.7812024Z 2025-03-04T21:04:05.7812353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:05.7812475Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:04:05.7812644Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:04:05.7812786Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:04:05.7812851Z 2025-03-04T21:04:05.7813169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:05.7813269Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:04:05.7813397Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:04:05.7813464Z 2025-03-04T21:04:05.7813779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:05.7813874Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:04:05.7814012Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:04:05.7814078Z 2025-03-04T21:04:05.7814451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:05.7814577Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:04:05.7814724Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:04:05.7814793Z 2025-03-04T21:04:05.7815128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:05.7815301Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:04:05.7815447Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:04:05.7815515Z 2025-03-04T21:04:05.7815883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:05.7816083Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:04:05.7816161Z 2025-03-04T21:04:05.7816509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:05.7816702Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:04:05.7816770Z 2025-03-04T21:04:05.7817162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:05.7817342Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:04:05.7817419Z 2025-03-04T21:04:05.7817838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:05.7818051Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:04:05.7818128Z 2025-03-04T21:04:05.7818572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:05.7818733Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:04:05.7818891Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:04:05.7819038Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:04:05.7819105Z 2025-03-04T21:04:05.7819492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:05.7819665Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:04:05.7819743Z 2025-03-04T21:04:05.7820062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:05.7820214Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:04:05.7820281Z 2025-03-04T21:04:05.7820629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:05.7820762Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:04:05.7820899Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:04:05.7821049Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:04:05.7821126Z 2025-03-04T21:04:05.7821463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:05.7821611Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:04:05.7821734Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:04:05.7821896Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:04:05.7821964Z 2025-03-04T21:04:05.7822290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:05.7822415Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:04:05.7822518Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:04:05.7822653Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:04:05.7822745Z 2025-03-04T21:04:05.7823068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:05.7823227Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:04:05.7823322Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:04:05.7823464Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:04:05.7823532Z 2025-03-04T21:04:05.7823853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:05.7824011Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:05.7824141Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:04:05.7824213Z 2025-03-04T21:04:05.7824535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:05.7824697Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:05.7824816Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:04:05.7824890Z 2025-03-04T21:04:05.7825199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:05.7825358Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:05.7825470Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:04:05.7825543Z 2025-03-04T21:04:05.7825860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:05.7826055Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:04:05.7826168Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:04:05.7826242Z 2025-03-04T21:04:05.7826608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:05.7826759Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:04:05.7826824Z 2025-03-04T21:04:05.7827169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:05.7827321Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:04:05.7827411Z 2025-03-04T21:04:05.7827769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:05.7827918Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:04:05.7828048Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:04:05.7828220Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:04:05.7828364Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:04:05.7828439Z 2025-03-04T21:04:05.7828783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:05.7828941Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:04:05.7829064Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:04:05.7829223Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:04:05.7829362Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:04:05.7829436Z 2025-03-04T21:04:05.7829766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:05.7829895Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:04:05.7830057Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:04:05.7830202Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:04:05.7830269Z 2025-03-04T21:04:05.7830611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:05.7830725Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:04:05.7830898Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:04:05.7831037Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:04:05.7831105Z 2025-03-04T21:04:05.7831424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:05.7831528Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:04:05.7831655Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:04:05.7831724Z 2025-03-04T21:04:05.7832044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:05.7832139Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:04:05.7832277Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:04:05.7832345Z 2025-03-04T21:04:05.7832661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:05.7832777Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:04:05.7832918Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:04:05.7832986Z 2025-03-04T21:04:05.7833335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:05.7833466Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:04:05.7833604Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:04:05.7833672Z 2025-03-04T21:04:05.7834025Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:05.7834214Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:04:05.7834287Z 2025-03-04T21:04:05.7834618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:05.7834801Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:04:05.7834868Z 2025-03-04T21:04:05.7835258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:05.7835428Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:04:05.7835500Z 2025-03-04T21:04:05.7835981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:04:05.7836124Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:04:05.7836192Z 2025-03-04T21:04:05.7836497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.7836641Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:04:05.7836714Z 2025-03-04T21:04:05.7837151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:05.7837276Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:04:05.7837384Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:04:05.7837511Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:04:05.7837576Z 2025-03-04T21:04:05.7838049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:05.7838184Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:05.7838439Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:04:05.7838507Z 2025-03-04T21:04:05.7838985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:05.7839163Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:05.7839229Z 2025-03-04T21:04:05.7839547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.7839688Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:04:05.7839761Z 2025-03-04T21:04:05.7840195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:05.7840322Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:04:05.7840431Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:04:05.7840560Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:04:05.7840625Z 2025-03-04T21:04:05.7841094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:05.7841242Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:05.7841485Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:04:05.7841550Z 2025-03-04T21:04:05.7842013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:05.7842177Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:05.7842247Z 2025-03-04T21:04:05.7842534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.7842666Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:04:05.7842730Z 2025-03-04T21:04:05.7843154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:05.7843267Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:04:05.7843381Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:04:05.7843494Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:04:05.7843565Z 2025-03-04T21:04:05.7844006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:05.7844146Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:05.7844379Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:04:05.7844450Z 2025-03-04T21:04:05.7844906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:05.7845077Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:05.7845149Z 2025-03-04T21:04:05.7845435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.7845582Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:04:05.7845661Z 2025-03-04T21:04:05.7846088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:05.7846200Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:04:05.7846314Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:04:05.7846429Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:04:05.7846501Z 2025-03-04T21:04:05.7846937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:05.7847091Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:05.7847323Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:04:05.7847393Z 2025-03-04T21:04:05.7847837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:05.7848005Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:05.7848070Z 2025-03-04T21:04:05.7848363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.7848483Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:04:05.7848555Z 2025-03-04T21:04:05.7848981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:05.7849101Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:04:05.7849204Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:04:05.7849327Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:04:05.7849396Z 2025-03-04T21:04:05.7849852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:05.7850013Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:04:05.7850251Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:04:05.7850317Z 2025-03-04T21:04:05.7850767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:05.7850952Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:05.7851018Z 2025-03-04T21:04:05.7851323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:05.7851449Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:04:05.7851524Z 2025-03-04T21:04:05.7851819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:05.7852216Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:04:05.7852283Z 2025-03-04T21:04:05.7852573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:05.7853031Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:04:05.7853106Z 2025-03-04T21:04:05.7853398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:05.7853605Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:04:05.7853670Z 2025-03-04T21:04:05.7854065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:04:05.7854272Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:04:05.7854352Z 2025-03-04T21:04:05.7854663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:04:05.7854826Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:04:05.7854896Z 2025-03-04T21:04:05.7855296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:04:05.7855439Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:04:05.7855517Z 2025-03-04T21:04:05.7856018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:04:05.7856164Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:04:05.7856289Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:04:05.7856455Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:04:05.7856592Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:04:05.7856669Z 2025-03-04T21:04:05.7857038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:04:05.7857164Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:04:05.7857261Z 2025-03-04T21:04:26.4548921Z 2025-03-04T21:04:26.4555098Z class GraphModule(torch.nn.Module): 2025-03-04T21:04:26.4562589Z def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:04:26.4567885Z l_features_p2_ = L_features_p2_ 2025-03-04T21:04:26.4569117Z l_features_p3_ = L_features_p3_ 2025-03-04T21:04:26.4569477Z l_features_p4_ = L_features_p4_ 2025-03-04T21:04:26.4569805Z l_features_p5_ = L_features_p5_ 2025-03-04T21:04:26.4570120Z l_features_p6_ = L_features_p6_ 2025-03-04T21:04:26.4570775Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:04:26.4571766Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:04:26.4572733Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:04:26.4573800Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:04:26.4574774Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:04:26.4575749Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:04:26.4576636Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:04:26.4577615Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:04:26.4578693Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:04:26.4579719Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:04:26.4580761Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:04:26.4581393Z 2025-03-04T21:04:26.4582348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.4583512Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:04:26.4584091Z 2025-03-04T21:04:26.4584767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.4585624Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:04:26.4586037Z 2025-03-04T21:04:26.4586983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.4588405Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:04:26.4588905Z 2025-03-04T21:04:26.4589603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.4590464Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:04:26.4590918Z 2025-03-04T21:04:26.4591782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:26.4592911Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:04:26.4593520Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:04:26.4594099Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:04:26.4594520Z 2025-03-04T21:04:26.4595295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:26.4596243Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:04:26.4596654Z 2025-03-04T21:04:26.4597411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:26.4598344Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:04:26.4598752Z 2025-03-04T21:04:26.4599599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:26.4600982Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:04:26.4601565Z 2025-03-04T21:04:26.4602489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:26.4603585Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:04:26.4604489Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:04:26.4605376Z add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:04:26.4605886Z x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:04:26.4606274Z 2025-03-04T21:04:26.4607239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.4608436Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:04:26.4608907Z 2025-03-04T21:04:26.4609651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.4610540Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:04:26.4610985Z 2025-03-04T21:04:26.4611920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.4613136Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:04:26.4613745Z 2025-03-04T21:04:26.4614466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.4615365Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:04:26.4615793Z 2025-03-04T21:04:26.4616606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:26.4617718Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:04:26.4618321Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:04:26.4618887Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:04:26.4619304Z 2025-03-04T21:04:26.4620055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:26.4620966Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:04:26.4621373Z 2025-03-04T21:04:26.4622102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:26.4623009Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:04:26.4623414Z 2025-03-04T21:04:26.4624237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:26.4625420Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:04:26.4625990Z 2025-03-04T21:04:26.4626877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:26.4627959Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:04:26.4628844Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:04:26.4629722Z add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:04:26.4630231Z x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4); add_1 = None 2025-03-04T21:04:26.4630624Z 2025-03-04T21:04:26.4631550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.4632709Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:04:26.4633155Z 2025-03-04T21:04:26.4633875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.4634742Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:04:26.4635159Z 2025-03-04T21:04:26.4636089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.4637317Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:04:26.4637786Z 2025-03-04T21:04:26.4638457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.4639324Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:04:26.4639745Z 2025-03-04T21:04:26.4640569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:26.4641680Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:04:26.4642270Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:04:26.4642762Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:04:26.4643157Z 2025-03-04T21:04:26.4643892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:26.4644798Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:04:26.4645203Z 2025-03-04T21:04:26.4645930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:26.4646829Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:04:26.4647232Z 2025-03-04T21:04:26.4648063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:26.4649200Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:04:26.4649764Z 2025-03-04T21:04:26.4650654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:26.4651735Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:04:26.4652642Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:04:26.4653637Z add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:04:26.4654174Z x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4); add_2 = None 2025-03-04T21:04:26.4654596Z 2025-03-04T21:04:26.4655548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.4656731Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:04:26.4657168Z 2025-03-04T21:04:26.4657935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.4658879Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:04:26.4659327Z 2025-03-04T21:04:26.4660289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.4661502Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:04:26.4661986Z 2025-03-04T21:04:26.4662671Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.4663566Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:04:26.4664002Z 2025-03-04T21:04:26.4664837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:26.4665974Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:04:26.4666546Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:04:26.4667030Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:04:26.4667424Z 2025-03-04T21:04:26.4668142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:26.4669037Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:04:26.4669434Z 2025-03-04T21:04:26.4670150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:26.4671025Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:04:26.4671416Z 2025-03-04T21:04:26.4672239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:26.4673369Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:04:26.4673906Z 2025-03-04T21:04:26.4674756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:26.4675796Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:04:26.4676582Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:04:26.4677373Z add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:04:26.4677837Z x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:04:26.4678202Z 2025-03-04T21:04:26.4679104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.4680218Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:04:26.4680659Z 2025-03-04T21:04:26.4681390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.4682268Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:04:26.4682691Z 2025-03-04T21:04:26.4683648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.4684825Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:04:26.4685279Z 2025-03-04T21:04:26.4685933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.4686785Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:04:26.4687212Z 2025-03-04T21:04:26.4688002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:26.4689385Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:04:26.4690026Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:04:26.4690577Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:04:26.4690978Z 2025-03-04T21:04:26.4691714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:26.4692631Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:04:26.4693037Z 2025-03-04T21:04:26.4693849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:26.4694769Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:04:26.4695157Z 2025-03-04T21:04:26.4695955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:26.4697116Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:04:26.4697668Z 2025-03-04T21:04:26.4698552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:26.4699625Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:04:26.4700494Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:04:26.4701347Z add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:04:26.4701849Z x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:04:26.4702250Z 2025-03-04T21:04:26.4702948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:04:26.4703812Z tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:04:26.4704345Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32); x_1 = None 2025-03-04T21:04:26.4704993Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32); x_2 = None 2025-03-04T21:04:26.4705510Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32); x_3 = None 2025-03-04T21:04:26.4705999Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32); x_4 = None 2025-03-04T21:04:26.4706385Z 2025-03-04T21:04:26.4706974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:26.4708350Z x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p2_ = None 2025-03-04T21:04:26.4709381Z 2025-03-04T21:04:26.4710000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:26.4710916Z x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False); x_5 = None 2025-03-04T21:04:26.4711442Z 2025-03-04T21:04:26.4712248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:26.4713726Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:26.4714697Z 2025-03-04T21:04:26.4715460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:26.4716904Z x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_6 = None 2025-03-04T21:04:26.4717804Z 2025-03-04T21:04:26.4718402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:26.4719653Z x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p3_ = None 2025-03-04T21:04:26.4720561Z 2025-03-04T21:04:26.4721152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:26.4722019Z x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False); x_7 = None 2025-03-04T21:04:26.4722526Z 2025-03-04T21:04:26.4723280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:26.4724854Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:26.4725761Z 2025-03-04T21:04:26.4726516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:26.4727993Z x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_8 = None 2025-03-04T21:04:26.4728915Z 2025-03-04T21:04:26.4729695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:26.4730956Z x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p4_ = None 2025-03-04T21:04:26.4731850Z 2025-03-04T21:04:26.4732464Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:26.4733370Z x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False); x_9 = None 2025-03-04T21:04:26.4733976Z 2025-03-04T21:04:26.4734829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:26.4736312Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:26.4737160Z 2025-03-04T21:04:26.4737871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:26.4739353Z x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_10 = None 2025-03-04T21:04:26.4740255Z 2025-03-04T21:04:26.4740845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:26.4742116Z x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p5_ = None 2025-03-04T21:04:26.4743018Z 2025-03-04T21:04:26.4743636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:26.4744534Z x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False); x_11 = None 2025-03-04T21:04:26.4745030Z 2025-03-04T21:04:26.4745841Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:26.4747301Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:26.4748110Z 2025-03-04T21:04:26.4748842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:26.4750180Z x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_12 = None 2025-03-04T21:04:26.4751020Z 2025-03-04T21:04:26.4751571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:26.4753100Z x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:04:26.4754248Z 2025-03-04T21:04:26.4754790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:26.4755692Z x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False); x_13 = None 2025-03-04T21:04:26.4756262Z 2025-03-04T21:04:26.4757093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:26.4759009Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:04:26.4760344Z 2025-03-04T21:04:26.4761130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:26.4763008Z x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:04:26.4764278Z 2025-03-04T21:04:26.4765033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:04:26.4766031Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:04:26.4766631Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:04:26.4767253Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:04:26.4767899Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:04:26.4768517Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:04:26.4769122Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:04:26.4769731Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:04:26.4770327Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:04:26.4770918Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:04:26.4771503Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:04:26.4771941Z 2025-03-04T21:04:26.4772873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:04:26.4774190Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304); x_15 = None 2025-03-04T21:04:26.4774996Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:04:26.4775737Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:04:26.4776432Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152); x_16 = None 2025-03-04T21:04:26.4777126Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:04:26.4777866Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:04:26.4778561Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76); x_17 = None 2025-03-04T21:04:26.4779211Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:04:26.4779904Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:04:26.4780557Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38); x_18 = None 2025-03-04T21:04:26.4781202Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:04:26.4781877Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:04:26.4782546Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19); x_19 = None 2025-03-04T21:04:26.4783167Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:04:26.4783835Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:04:26.4784326Z 2025-03-04T21:04:26.4785232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:26.4786450Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:04:26.4787008Z 2025-03-04T21:04:26.4787940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:26.4789300Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:04:26.4789936Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:04:26.4790536Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:04:26.4790974Z 2025-03-04T21:04:26.4791813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:26.4792914Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:04:26.4793427Z 2025-03-04T21:04:26.4794130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:26.4795042Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:04:26.4795500Z 2025-03-04T21:04:26.4796203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:26.4797224Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:04:26.4797767Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:26.4798356Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:04:26.4798810Z 2025-03-04T21:04:26.4799520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:26.4800472Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:04:26.4801042Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:04:26.4801616Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:04:26.4802072Z 2025-03-04T21:04:26.4802767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:26.4803660Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:26.4804106Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:04:26.4804696Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:04:26.4805109Z 2025-03-04T21:04:26.4805818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:26.4806797Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:04:26.4807286Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:04:26.4807751Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:04:26.4808170Z 2025-03-04T21:04:26.4808899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:26.4809817Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:26.4810381Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:04:26.4810767Z 2025-03-04T21:04:26.4811479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:26.4812402Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:26.4812976Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:04:26.4813367Z 2025-03-04T21:04:26.4814147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:26.4815109Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:26.4815688Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:04:26.4816092Z 2025-03-04T21:04:26.4816832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:26.4817878Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:04:26.4818518Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:04:26.4818926Z 2025-03-04T21:04:26.4819721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:26.4820791Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:04:26.4821251Z 2025-03-04T21:04:26.4822038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:26.4823038Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:04:26.4823492Z 2025-03-04T21:04:26.4824315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:26.4825373Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:04:26.4825964Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:04:26.4826552Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:04:26.4827166Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:04:26.4827600Z 2025-03-04T21:04:26.4828357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:26.4829319Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:04:26.4829909Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:04:26.4830480Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:04:26.4831081Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:04:26.4831524Z 2025-03-04T21:04:26.4832261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:26.4833155Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:04:26.4833731Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:04:26.4834329Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:04:26.4834760Z 2025-03-04T21:04:26.4835503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:26.4836406Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:04:26.4836987Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:04:26.4837613Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:04:26.4838046Z 2025-03-04T21:04:26.4838745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:26.4839565Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:04:26.4840011Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:04:26.4840403Z 2025-03-04T21:04:26.4841101Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:26.4841928Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:04:26.4842363Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:04:26.4842751Z 2025-03-04T21:04:26.4843490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:26.4844332Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:04:26.4844842Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:04:26.4845256Z 2025-03-04T21:04:26.4845939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:26.4846815Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:04:26.4847341Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:04:26.4847749Z 2025-03-04T21:04:26.4848523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:26.4849567Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:04:26.4850064Z 2025-03-04T21:04:26.4850803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:26.4851799Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:04:26.4852306Z 2025-03-04T21:04:26.4853123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:26.4854347Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:04:26.4854863Z 2025-03-04T21:04:26.4855766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:26.4856993Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:04:26.4857564Z 2025-03-04T21:04:26.4858510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:26.4859693Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:04:26.4860334Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:04:26.4860943Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:04:26.4861391Z 2025-03-04T21:04:26.4862224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:26.4863321Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:04:26.4863822Z 2025-03-04T21:04:26.4864540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:26.4865478Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:04:26.4865939Z 2025-03-04T21:04:26.4866658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:26.4867638Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:04:26.4868203Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:04:26.4868796Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:04:26.4869247Z 2025-03-04T21:04:26.4869953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:26.4870881Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:04:26.4871427Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:04:26.4871994Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:04:26.4872442Z 2025-03-04T21:04:26.4873135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:26.4874004Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:04:26.4874457Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:04:26.4874923Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:04:26.4875340Z 2025-03-04T21:04:26.4876051Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:26.4877016Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:04:26.4877531Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:04:26.4877999Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:04:26.4878415Z 2025-03-04T21:04:26.4879133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:26.4880047Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:26.4880607Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:04:26.4880998Z 2025-03-04T21:04:26.4881676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:26.4882578Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:26.4883124Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:04:26.4883506Z 2025-03-04T21:04:26.4884181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:26.4885081Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:26.4885629Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:04:26.4886010Z 2025-03-04T21:04:26.4886700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:26.4887600Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:04:26.4888379Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:04:26.4888789Z 2025-03-04T21:04:26.4889540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:26.4890647Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:04:26.4891087Z 2025-03-04T21:04:26.4891837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:26.4892815Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:04:26.4893254Z 2025-03-04T21:04:26.4894165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:26.4895196Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:04:26.4895760Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:04:26.4896354Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:04:26.4896980Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:04:26.4897422Z 2025-03-04T21:04:26.4898195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:26.4899181Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:04:26.4899816Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:04:26.4900407Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:04:26.4900990Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:04:26.4901414Z 2025-03-04T21:04:26.4902097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:26.4902967Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:04:26.4903516Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:04:26.4904086Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:04:26.4904515Z 2025-03-04T21:04:26.4905239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:26.4906104Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:04:26.4906657Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:04:26.4907248Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:04:26.4907661Z 2025-03-04T21:04:26.4908336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:26.4909122Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:04:26.4909553Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:04:26.4909951Z 2025-03-04T21:04:26.4910618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:26.4911392Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:04:26.4911816Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:04:26.4912303Z 2025-03-04T21:04:26.4912964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:26.4913756Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:04:26.4914247Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:04:26.4914659Z 2025-03-04T21:04:26.4915293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:26.4916032Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:04:26.4916339Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:04:26.4916591Z 2025-03-04T21:04:26.4917022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:26.4917605Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:04:26.4917916Z 2025-03-04T21:04:26.4918330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:26.4918908Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:04:26.4919192Z 2025-03-04T21:04:26.4919650Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:26.4920255Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:04:26.4920535Z 2025-03-04T21:04:26.4921202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:26.4921856Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:04:26.4922186Z 2025-03-04T21:04:26.4922728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:26.4923381Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:04:26.4923750Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:04:26.4924110Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:04:26.4924381Z 2025-03-04T21:04:26.4924858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:26.4925632Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:04:26.4925932Z 2025-03-04T21:04:26.4926341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:26.4926858Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:04:26.4927129Z 2025-03-04T21:04:26.4927561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:26.4928072Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:04:26.4928391Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:04:26.4928726Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:04:26.4929002Z 2025-03-04T21:04:26.4929558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:26.4930275Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:04:26.4930693Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:04:26.4931141Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:04:26.4931521Z 2025-03-04T21:04:26.4932091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:26.4932832Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:04:26.4933224Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:04:26.4933708Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:04:26.4934124Z 2025-03-04T21:04:26.4934744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:26.4935502Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:04:26.4935903Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:04:26.4936291Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:04:26.4936641Z 2025-03-04T21:04:26.4937232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:26.4938096Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:26.4938623Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:04:26.4938994Z 2025-03-04T21:04:26.4939682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:26.4940479Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:26.4940929Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:04:26.4941240Z 2025-03-04T21:04:26.4941799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:26.4942681Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:26.4943273Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:04:26.4943682Z 2025-03-04T21:04:26.4944405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:26.4945389Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:04:26.4945895Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:04:26.4946242Z 2025-03-04T21:04:26.4946955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:26.4947800Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:04:26.4948204Z 2025-03-04T21:04:26.4948895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:26.4949722Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:04:26.4950124Z 2025-03-04T21:04:26.4950791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:26.4951625Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:04:26.4952094Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:04:26.4952617Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:04:26.4953153Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:04:26.4953541Z 2025-03-04T21:04:26.4954221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:26.4955160Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:04:26.4955630Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:04:26.4956139Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:04:26.4956701Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:04:26.4957080Z 2025-03-04T21:04:26.4957743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:26.4958512Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:04:26.4959024Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:04:26.4959571Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:04:26.4959964Z 2025-03-04T21:04:26.4960647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:26.4961466Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:04:26.4961979Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:04:26.4962537Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:04:26.4962920Z 2025-03-04T21:04:26.4963552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:26.4964287Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:04:26.4964729Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:04:26.4965116Z 2025-03-04T21:04:26.4965749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:26.4966517Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:04:26.4966988Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:04:26.4967378Z 2025-03-04T21:04:26.4968046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:26.4968859Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:04:26.4969357Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:04:26.4969746Z 2025-03-04T21:04:26.4970431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:26.4971269Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:04:26.4971749Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:04:26.4972149Z 2025-03-04T21:04:26.4972905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:26.4974109Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:04:26.4974654Z 2025-03-04T21:04:26.4975385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:26.4976419Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:04:26.4976904Z 2025-03-04T21:04:26.4977763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:26.4978905Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:04:26.4979405Z 2025-03-04T21:04:26.4980271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:26.4981490Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:04:26.4982035Z 2025-03-04T21:04:26.4982930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:26.4984056Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:04:26.4984669Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:04:26.4985256Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:04:26.4985689Z 2025-03-04T21:04:26.4986519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:26.4987577Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:04:26.4988319Z 2025-03-04T21:04:26.4989058Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:26.4989860Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:04:26.4990293Z 2025-03-04T21:04:26.4991158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:26.4992025Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:04:26.4992525Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:04:26.4993076Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:04:26.4993526Z 2025-03-04T21:04:26.4994324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:26.4995229Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:04:26.4995712Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:04:26.4996284Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:04:26.4996751Z 2025-03-04T21:04:26.4997472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:26.4998354Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:04:26.4998799Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:04:26.4999243Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:04:26.4999681Z 2025-03-04T21:04:26.5000343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:26.5001206Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:04:26.5001678Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:04:26.5002111Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:04:26.5002508Z 2025-03-04T21:04:26.5003192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:26.5004066Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:26.5004626Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:04:26.5005012Z 2025-03-04T21:04:26.5005682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:26.5006554Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:26.5007074Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:04:26.5007395Z 2025-03-04T21:04:26.5008007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:26.5008814Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:26.5009312Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:04:26.5009672Z 2025-03-04T21:04:26.5010324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:26.5011174Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:04:26.5011722Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:04:26.5012065Z 2025-03-04T21:04:26.5012805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:26.5013788Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:04:26.5014229Z 2025-03-04T21:04:26.5015000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:26.5015980Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:04:26.5016427Z 2025-03-04T21:04:26.5017124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:26.5017990Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:04:26.5018491Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:04:26.5019031Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:04:26.5019596Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:04:26.5020001Z 2025-03-04T21:04:26.5020732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:26.5021671Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:04:26.5022116Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:04:26.5022639Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:04:26.5023200Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:04:26.5023618Z 2025-03-04T21:04:26.5024328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:26.5025193Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:04:26.5025739Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:04:26.5026331Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:04:26.5026749Z 2025-03-04T21:04:26.5027469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:26.5028353Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:04:26.5028909Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:04:26.5029498Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:04:26.5029903Z 2025-03-04T21:04:26.5030596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:26.5031389Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:04:26.5031828Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:04:26.5032213Z 2025-03-04T21:04:26.5032883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:26.5033720Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:04:26.5034169Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:04:26.5034559Z 2025-03-04T21:04:26.5035281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:26.5036139Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:04:26.5036641Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:04:26.5037101Z 2025-03-04T21:04:26.5037814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:26.5038640Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:04:26.5039156Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:04:26.5039571Z 2025-03-04T21:04:26.5040336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:26.5041368Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:04:26.5041858Z 2025-03-04T21:04:26.5042574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:26.5043584Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:04:26.5044048Z 2025-03-04T21:04:26.5044847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:26.5045893Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:04:26.5046369Z 2025-03-04T21:04:26.5047194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:26.5048358Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:04:26.5048902Z 2025-03-04T21:04:26.5049834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:26.5050948Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:04:26.5051558Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:04:26.5052144Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:04:26.5052566Z 2025-03-04T21:04:26.5053368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:26.5054583Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:04:26.5055078Z 2025-03-04T21:04:26.5055775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:26.5056686Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:04:26.5057196Z 2025-03-04T21:04:26.5057896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:26.5058778Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:04:26.5059291Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:04:26.5059850Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:04:26.5060304Z 2025-03-04T21:04:26.5061094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:26.5061897Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:04:26.5062311Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:04:26.5062809Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:04:26.5063194Z 2025-03-04T21:04:26.5063851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:26.5064675Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:04:26.5065092Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:04:26.5065576Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:04:26.5065967Z 2025-03-04T21:04:26.5066628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:26.5067501Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:04:26.5067958Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:04:26.5068382Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:04:26.5068764Z 2025-03-04T21:04:26.5069434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:26.5070267Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:26.5070781Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:04:26.5071160Z 2025-03-04T21:04:26.5071810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:26.5072658Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:26.5073168Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:04:26.5073512Z 2025-03-04T21:04:26.5074136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:26.5074992Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:26.5075488Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:04:26.5075841Z 2025-03-04T21:04:26.5076442Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:26.5077319Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:04:26.5077886Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:04:26.5078334Z 2025-03-04T21:04:26.5079068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:26.5079973Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:04:26.5080358Z 2025-03-04T21:04:26.5081059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:26.5081949Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:04:26.5082353Z 2025-03-04T21:04:26.5083054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:26.5083936Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:04:26.5084429Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:04:26.5084963Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:04:26.5085528Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:04:26.5085946Z 2025-03-04T21:04:26.5086687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:26.5087644Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:04:26.5088365Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:04:26.5088917Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:04:26.5089512Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:04:26.5089935Z 2025-03-04T21:04:26.5090660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:26.5091550Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:04:26.5092105Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:04:26.5092698Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:04:26.5093114Z 2025-03-04T21:04:26.5093954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:26.5094849Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:04:26.5095408Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:04:26.5096011Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:04:26.5096425Z 2025-03-04T21:04:26.5097122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:26.5097941Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:04:26.5098386Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:04:26.5098773Z 2025-03-04T21:04:26.5099465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:26.5100587Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:04:26.5101127Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:04:26.5101634Z 2025-03-04T21:04:26.5102551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:26.5103673Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:04:26.5104267Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:04:26.5104681Z 2025-03-04T21:04:26.5105267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:26.5105994Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:04:26.5106414Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:04:26.5106804Z 2025-03-04T21:04:26.5107537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:26.5108533Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:04:26.5109028Z 2025-03-04T21:04:26.5109982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:26.5110943Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:04:26.5111419Z 2025-03-04T21:04:26.5112222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:26.5113303Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:04:26.5113802Z 2025-03-04T21:04:26.5114872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:04:26.5116029Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:04:26.5116416Z 2025-03-04T21:04:26.5117029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5117844Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:04:26.5118243Z 2025-03-04T21:04:26.5119088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:26.5120065Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:04:26.5120482Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:04:26.5120896Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:04:26.5121269Z 2025-03-04T21:04:26.5122183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:26.5123325Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:26.5124108Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:04:26.5124687Z 2025-03-04T21:04:26.5125624Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:26.5126812Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:26.5127278Z 2025-03-04T21:04:26.5127990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5128843Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:04:26.5129231Z 2025-03-04T21:04:26.5130133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:26.5131170Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:04:26.5131623Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:04:26.5132079Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:04:26.5132461Z 2025-03-04T21:04:26.5133446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:26.5134812Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:26.5135571Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:04:26.5136198Z 2025-03-04T21:04:26.5137165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:26.5138397Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:26.5138872Z 2025-03-04T21:04:26.5139541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5140377Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:04:26.5140780Z 2025-03-04T21:04:26.5141716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:26.5142663Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:04:26.5143050Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:04:26.5143478Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:04:26.5143822Z 2025-03-04T21:04:26.5144761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:26.5145897Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:26.5146604Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:04:26.5147184Z 2025-03-04T21:04:26.5148206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:26.5149402Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:26.5149847Z 2025-03-04T21:04:26.5150416Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5151257Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:04:26.5151681Z 2025-03-04T21:04:26.5152561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:26.5153571Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:04:26.5154013Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:04:26.5154461Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:04:26.5154821Z 2025-03-04T21:04:26.5155738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:26.5156842Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:26.5157553Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:04:26.5158141Z 2025-03-04T21:04:26.5159091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:26.5160254Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:26.5160705Z 2025-03-04T21:04:26.5161343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5162148Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:04:26.5162537Z 2025-03-04T21:04:26.5163365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:26.5164379Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:04:26.5164828Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:04:26.5165285Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:04:26.5165667Z 2025-03-04T21:04:26.5166636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:26.5167874Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:04:26.5168681Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:04:26.5169288Z 2025-03-04T21:04:26.5170329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:26.5171554Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:26.5172038Z 2025-03-04T21:04:26.5172703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5173643Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:04:26.5174079Z 2025-03-04T21:04:26.5174778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:26.5176122Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:04:26.5177000Z 2025-03-04T21:04:26.5177645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:26.5179070Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:04:26.5180123Z 2025-03-04T21:04:26.5180763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:26.5181707Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:04:26.5182225Z 2025-03-04T21:04:26.5183061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:04:26.5184111Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:04:26.5184544Z 2025-03-04T21:04:26.5185197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:04:26.5186059Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:04:26.5186484Z 2025-03-04T21:04:26.5187281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:04:26.5188463Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:04:26.5188880Z 2025-03-04T21:04:26.5189915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:04:26.5191122Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:04:26.5191643Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:04:26.5192202Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:04:26.5192778Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:04:26.5193180Z 2025-03-04T21:04:26.5193959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:04:26.5195020Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:04:26.5195391Z 2025-03-04T21:04:26.5195757Z 2025-03-04T21:04:26.5195891Z class GraphModule(torch.nn.Module): 2025-03-04T21:04:26.5199901Z def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:04:26.5203914Z l_features_p2_ = L_features_p2_ 2025-03-04T21:04:26.5204254Z l_features_p3_ = L_features_p3_ 2025-03-04T21:04:26.5204589Z l_features_p4_ = L_features_p4_ 2025-03-04T21:04:26.5204900Z l_features_p5_ = L_features_p5_ 2025-03-04T21:04:26.5205218Z l_features_p6_ = L_features_p6_ 2025-03-04T21:04:26.5205823Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:04:26.5206764Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:04:26.5207708Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:04:26.5208612Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:04:26.5209564Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:04:26.5210468Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:04:26.5211273Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:04:26.5212208Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:04:26.5213218Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:04:26.5214273Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:04:26.5215221Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:04:26.5215845Z 2025-03-04T21:04:26.5216786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.5217990Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:04:26.5218428Z 2025-03-04T21:04:26.5219070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5219900Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:04:26.5220298Z 2025-03-04T21:04:26.5221218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.5222352Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:04:26.5222790Z 2025-03-04T21:04:26.5223439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5224284Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:04:26.5224701Z 2025-03-04T21:04:26.5225491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:26.5226550Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:04:26.5227135Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:04:26.5227582Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:04:26.5227970Z 2025-03-04T21:04:26.5228694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:26.5229580Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:04:26.5229965Z 2025-03-04T21:04:26.5230678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:26.5231560Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:04:26.5231952Z 2025-03-04T21:04:26.5232755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:26.5233892Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:04:26.5234428Z 2025-03-04T21:04:26.5235293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:26.5236336Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:04:26.5237192Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:04:26.5238022Z add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:04:26.5238500Z x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:04:26.5238871Z 2025-03-04T21:04:26.5239777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.5240943Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:04:26.5241469Z 2025-03-04T21:04:26.5242120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5242991Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:04:26.5243422Z 2025-03-04T21:04:26.5244388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.5245564Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:04:26.5246015Z 2025-03-04T21:04:26.5246678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5247531Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:04:26.5247995Z 2025-03-04T21:04:26.5248817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:26.5249922Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:04:26.5250533Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:04:26.5251038Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:04:26.5251445Z 2025-03-04T21:04:26.5252169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:26.5253095Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:04:26.5253497Z 2025-03-04T21:04:26.5254388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:26.5255335Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:04:26.5255732Z 2025-03-04T21:04:26.5256561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:26.5257731Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:04:26.5258197Z 2025-03-04T21:04:26.5259014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:26.5260071Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:04:26.5260945Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:04:26.5261793Z add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:04:26.5262284Z x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4); add_1 = None 2025-03-04T21:04:26.5262676Z 2025-03-04T21:04:26.5263613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.5264830Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:04:26.5265271Z 2025-03-04T21:04:26.5265928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5266778Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:04:26.5267193Z 2025-03-04T21:04:26.5268165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.5269311Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:04:26.5269734Z 2025-03-04T21:04:26.5270360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5271202Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:04:26.5271622Z 2025-03-04T21:04:26.5272423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:26.5273525Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:04:26.5274069Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:04:26.5274535Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:04:26.5274910Z 2025-03-04T21:04:26.5275613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:26.5276479Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:04:26.5276836Z 2025-03-04T21:04:26.5277525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:26.5278391Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:04:26.5278781Z 2025-03-04T21:04:26.5279572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:26.5280702Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:04:26.5281247Z 2025-03-04T21:04:26.5282124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:26.5283152Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:04:26.5284016Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:04:26.5284848Z add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:04:26.5285334Z x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4); add_2 = None 2025-03-04T21:04:26.5285714Z 2025-03-04T21:04:26.5286603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.5287796Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:04:26.5288502Z 2025-03-04T21:04:26.5289162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5290011Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:04:26.5290438Z 2025-03-04T21:04:26.5291482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.5292690Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:04:26.5293133Z 2025-03-04T21:04:26.5293897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5294865Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:04:26.5295310Z 2025-03-04T21:04:26.5296005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:26.5296416Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:04:26.5296600Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:04:26.5296912Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:04:26.5297011Z 2025-03-04T21:04:26.5297623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:26.5297829Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:04:26.5297931Z 2025-03-04T21:04:26.5298532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:26.5298745Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:04:26.5298836Z 2025-03-04T21:04:26.5299531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:26.5299911Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:04:26.5300002Z 2025-03-04T21:04:26.5300763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:26.5300972Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:04:26.5301545Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:04:26.5301741Z add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:04:26.5301938Z x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:04:26.5302027Z 2025-03-04T21:04:26.5302825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.5303137Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:04:26.5303241Z 2025-03-04T21:04:26.5303761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5303997Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:04:26.5304087Z 2025-03-04T21:04:26.5304916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:04:26.5305186Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:04:26.5305285Z 2025-03-04T21:04:26.5305813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5306048Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:04:26.5306137Z 2025-03-04T21:04:26.5306810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:04:26.5307141Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:04:26.5307307Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:04:26.5307529Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:04:26.5307630Z 2025-03-04T21:04:26.5308220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:04:26.5308437Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:04:26.5308524Z 2025-03-04T21:04:26.5309113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:04:26.5309318Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:04:26.5309415Z 2025-03-04T21:04:26.5310092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:04:26.5310458Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:04:26.5310552Z 2025-03-04T21:04:26.5311285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:04:26.5311498Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:04:26.5312044Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:04:26.5312235Z add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:04:26.5312420Z x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:04:26.5312523Z 2025-03-04T21:04:26.5313044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:04:26.5313246Z tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:04:26.5313478Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32); x_1 = None 2025-03-04T21:04:26.5313683Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32); x_2 = None 2025-03-04T21:04:26.5313871Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32); x_3 = None 2025-03-04T21:04:26.5314065Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32); x_4 = None 2025-03-04T21:04:26.5314157Z 2025-03-04T21:04:26.5314584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:26.5315247Z x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p2_ = None 2025-03-04T21:04:26.5315366Z 2025-03-04T21:04:26.5315824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:26.5316137Z x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False); x_5 = None 2025-03-04T21:04:26.5316222Z 2025-03-04T21:04:26.5316886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:26.5317600Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:26.5317729Z 2025-03-04T21:04:26.5318357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:26.5319073Z x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_6 = None 2025-03-04T21:04:26.5319169Z 2025-03-04T21:04:26.5319608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:26.5320333Z x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p3_ = None 2025-03-04T21:04:26.5320426Z 2025-03-04T21:04:26.5320903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:26.5321179Z x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False); x_7 = None 2025-03-04T21:04:26.5321270Z 2025-03-04T21:04:26.5321927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:26.5322627Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:26.5322718Z 2025-03-04T21:04:26.5323336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:26.5324083Z x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_8 = None 2025-03-04T21:04:26.5324180Z 2025-03-04T21:04:26.5324622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:26.5325391Z x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p4_ = None 2025-03-04T21:04:26.5325490Z 2025-03-04T21:04:26.5325960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:26.5326248Z x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False); x_9 = None 2025-03-04T21:04:26.5326329Z 2025-03-04T21:04:26.5326972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:26.5327638Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:26.5327729Z 2025-03-04T21:04:26.5328369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:26.5329035Z x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_10 = None 2025-03-04T21:04:26.5329125Z 2025-03-04T21:04:26.5329577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:26.5330273Z x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p5_ = None 2025-03-04T21:04:26.5330366Z 2025-03-04T21:04:26.5330850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:26.5331156Z x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False); x_11 = None 2025-03-04T21:04:26.5331243Z 2025-03-04T21:04:26.5332041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:26.5332761Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:04:26.5332855Z 2025-03-04T21:04:26.5333650Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:26.5334370Z x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_12 = None 2025-03-04T21:04:26.5334478Z 2025-03-04T21:04:26.5334995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:04:26.5336066Z x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:04:26.5336164Z 2025-03-04T21:04:26.5336711Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:04:26.5337051Z x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False); x_13 = None 2025-03-04T21:04:26.5337161Z 2025-03-04T21:04:26.5337844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:04:26.5339022Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:04:26.5339126Z 2025-03-04T21:04:26.5339820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:04:26.5340905Z x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:04:26.5340994Z 2025-03-04T21:04:26.5341600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:04:26.5341872Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:04:26.5342115Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:04:26.5342403Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:04:26.5342639Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:04:26.5342885Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:04:26.5343123Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:04:26.5343362Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:04:26.5343588Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:04:26.5343823Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:04:26.5344042Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:04:26.5344140Z 2025-03-04T21:04:26.5344900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:04:26.5345223Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304); x_15 = None 2025-03-04T21:04:26.5345542Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:04:26.5345846Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:04:26.5346118Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152); x_16 = None 2025-03-04T21:04:26.5346454Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:04:26.5346770Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:04:26.5347019Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76); x_17 = None 2025-03-04T21:04:26.5347301Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:04:26.5347605Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:04:26.5347841Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38); x_18 = None 2025-03-04T21:04:26.5348116Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:04:26.5348404Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:04:26.5348667Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19); x_19 = None 2025-03-04T21:04:26.5348926Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:04:26.5349204Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:04:26.5349294Z 2025-03-04T21:04:26.5350034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:26.5350379Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:04:26.5350482Z 2025-03-04T21:04:26.5351237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:26.5351496Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:04:26.5351728Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:04:26.5351967Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:04:26.5352059Z 2025-03-04T21:04:26.5352718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:26.5352996Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:04:26.5353098Z 2025-03-04T21:04:26.5353636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:26.5353854Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:04:26.5353939Z 2025-03-04T21:04:26.5354564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:26.5354778Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:04:26.5354974Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:26.5355235Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:04:26.5355325Z 2025-03-04T21:04:26.5355926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:26.5356138Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:04:26.5356375Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:04:26.5356632Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:04:26.5356730Z 2025-03-04T21:04:26.5357296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:26.5357512Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:26.5357644Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:04:26.5357854Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:04:26.5357942Z 2025-03-04T21:04:26.5358496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:26.5358774Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:04:26.5358917Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:04:26.5359120Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:04:26.5359222Z 2025-03-04T21:04:26.5359781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:26.5360048Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:26.5360230Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:04:26.5360326Z 2025-03-04T21:04:26.5360868Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:26.5361135Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:26.5361320Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:04:26.5361419Z 2025-03-04T21:04:26.5361950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:26.5362214Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:26.5362409Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:04:26.5362508Z 2025-03-04T21:04:26.5363042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:26.5363368Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:04:26.5363548Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:04:26.5363644Z 2025-03-04T21:04:26.5364288Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:26.5364533Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:04:26.5364621Z 2025-03-04T21:04:26.5365223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:26.5365455Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:04:26.5365547Z 2025-03-04T21:04:26.5366219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:26.5366474Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:04:26.5366691Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:04:26.5366947Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:04:26.5367191Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:04:26.5367283Z 2025-03-04T21:04:26.5367909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:26.5368167Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:04:26.5368377Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:04:26.5368620Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:04:26.5368847Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:04:26.5368936Z 2025-03-04T21:04:26.5369540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:26.5369725Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:04:26.5370004Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:04:26.5370215Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:04:26.5370319Z 2025-03-04T21:04:26.5370906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:26.5371103Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:04:26.5371384Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:04:26.5371615Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:04:26.5371705Z 2025-03-04T21:04:26.5372264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:26.5372410Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:04:26.5372617Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:04:26.5372712Z 2025-03-04T21:04:26.5373270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:26.5373421Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:04:26.5373722Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:04:26.5373823Z 2025-03-04T21:04:26.5374448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:26.5374634Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:04:26.5374853Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:04:26.5374945Z 2025-03-04T21:04:26.5375509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:26.5375714Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:04:26.5375920Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:04:26.5376004Z 2025-03-04T21:04:26.5376581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:26.5376868Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:04:26.5376961Z 2025-03-04T21:04:26.5377522Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:26.5377789Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:04:26.5377901Z 2025-03-04T21:04:26.5378580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:26.5378874Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:04:26.5378964Z 2025-03-04T21:04:26.5379678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:26.5380031Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:04:26.5380127Z 2025-03-04T21:04:26.5380881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:26.5381131Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:04:26.5381368Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:04:26.5381590Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:04:26.5381675Z 2025-03-04T21:04:26.5382396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:26.5382695Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:04:26.5382790Z 2025-03-04T21:04:26.5383341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:26.5383599Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:04:26.5383685Z 2025-03-04T21:04:26.5384280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:26.5384492Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:04:26.5384706Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:04:26.5384951Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:04:26.5385046Z 2025-03-04T21:04:26.5385611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:26.5385844Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:04:26.5386034Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:04:26.5386293Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:04:26.5386383Z 2025-03-04T21:04:26.5386928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:26.5387121Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:04:26.5387270Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:04:26.5387482Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:04:26.5387582Z 2025-03-04T21:04:26.5388349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:26.5388613Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:04:26.5388759Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:04:26.5388978Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:04:26.5389071Z 2025-03-04T21:04:26.5389622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:26.5389873Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:26.5390064Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:04:26.5390155Z 2025-03-04T21:04:26.5390698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:26.5390961Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:26.5391143Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:04:26.5391239Z 2025-03-04T21:04:26.5391770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:26.5392031Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:26.5392219Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:04:26.5392320Z 2025-03-04T21:04:26.5392830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:26.5393151Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:04:26.5393336Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:04:26.5393432Z 2025-03-04T21:04:26.5394147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:26.5394385Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:04:26.5394472Z 2025-03-04T21:04:26.5395084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:26.5395307Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:04:26.5395451Z 2025-03-04T21:04:26.5396102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:26.5396340Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:04:26.5396543Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:04:26.5396816Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:04:26.5397046Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:04:26.5397142Z 2025-03-04T21:04:26.5397755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:26.5398030Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:04:26.5398232Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:04:26.5398499Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:04:26.5398727Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:04:26.5398824Z 2025-03-04T21:04:26.5399401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:26.5399590Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:04:26.5399853Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:04:26.5400091Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:04:26.5400189Z 2025-03-04T21:04:26.5400775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:26.5400968Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:04:26.5401244Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:04:26.5401466Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:04:26.5401553Z 2025-03-04T21:04:26.5402099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:26.5402247Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:04:26.5402449Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:04:26.5402542Z 2025-03-04T21:04:26.5403096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:26.5403239Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:04:26.5403469Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:04:26.5403558Z 2025-03-04T21:04:26.5404108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:26.5404292Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:04:26.5404513Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:04:26.5404602Z 2025-03-04T21:04:26.5405171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:26.5405385Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:04:26.5405598Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:04:26.5405688Z 2025-03-04T21:04:26.5406310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:26.5406625Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:04:26.5406720Z 2025-03-04T21:04:26.5407305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:26.5407617Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:04:26.5407696Z 2025-03-04T21:04:26.5408246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:26.5408520Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:04:26.5408615Z 2025-03-04T21:04:26.5409291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:26.5409638Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:04:26.5409728Z 2025-03-04T21:04:26.5410492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:26.5410737Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:04:26.5410991Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:04:26.5411209Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:04:26.5411308Z 2025-03-04T21:04:26.5411949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:26.5412222Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:04:26.5412323Z 2025-03-04T21:04:26.5412857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:26.5413100Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:04:26.5413189Z 2025-03-04T21:04:26.5413947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:26.5414164Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:04:26.5414379Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:04:26.5414624Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:04:26.5414724Z 2025-03-04T21:04:26.5415352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:26.5415587Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:04:26.5415782Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:04:26.5416062Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:04:26.5416157Z 2025-03-04T21:04:26.5416732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:26.5416927Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:04:26.5417075Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:04:26.5417296Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:04:26.5417426Z 2025-03-04T21:04:26.5417999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:26.5418258Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:04:26.5418402Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:04:26.5418624Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:04:26.5418713Z 2025-03-04T21:04:26.5419289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:26.5419532Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:26.5419723Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:04:26.5419822Z 2025-03-04T21:04:26.5420367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:26.5420611Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:26.5420797Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:04:26.5420887Z 2025-03-04T21:04:26.5421424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:26.5421651Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:26.5421828Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:04:26.5421920Z 2025-03-04T21:04:26.5422462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:26.5422767Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:04:26.5422955Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:04:26.5423055Z 2025-03-04T21:04:26.5423715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:26.5423956Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:04:26.5424047Z 2025-03-04T21:04:26.5424655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:26.5424903Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:04:26.5425027Z 2025-03-04T21:04:26.5425621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:26.5425847Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:04:26.5426043Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:04:26.5426305Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:04:26.5426531Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:04:26.5426626Z 2025-03-04T21:04:26.5427250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:26.5427512Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:04:26.5427705Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:04:26.5427941Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:04:26.5428158Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:04:26.5428253Z 2025-03-04T21:04:26.5428829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:26.5429017Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:04:26.5429276Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:04:26.5429500Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:04:26.5429591Z 2025-03-04T21:04:26.5430187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:26.5430364Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:04:26.5430644Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:04:26.5430851Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:04:26.5430953Z 2025-03-04T21:04:26.5431494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:26.5431653Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:04:26.5431844Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:04:26.5431942Z 2025-03-04T21:04:26.5432485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:26.5432682Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:04:26.5432867Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:04:26.5432969Z 2025-03-04T21:04:26.5433499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:26.5433694Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:04:26.5433909Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:04:26.5434035Z 2025-03-04T21:04:26.5434592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:26.5434784Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:04:26.5435003Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:04:26.5435108Z 2025-03-04T21:04:26.5435718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:26.5436035Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:04:26.5436132Z 2025-03-04T21:04:26.5436717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:26.5437022Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:04:26.5437113Z 2025-03-04T21:04:26.5437792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:26.5438079Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:04:26.5438179Z 2025-03-04T21:04:26.5438874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:26.5439233Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:04:26.5439323Z 2025-03-04T21:04:26.5440099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:26.5440340Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:04:26.5440593Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:04:26.5440815Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:04:26.5440909Z 2025-03-04T21:04:26.5441568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:26.5441863Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:04:26.5441947Z 2025-03-04T21:04:26.5442501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:26.5442737Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:04:26.5442870Z 2025-03-04T21:04:26.5443421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:26.5443639Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:04:26.5443844Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:04:26.5444104Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:04:26.5444196Z 2025-03-04T21:04:26.5444814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:26.5445041Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:04:26.5445248Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:04:26.5445504Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:04:26.5445603Z 2025-03-04T21:04:26.5446152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:26.5446351Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:04:26.5446484Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:04:26.5446746Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:04:26.5446836Z 2025-03-04T21:04:26.5447407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:26.5447650Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:04:26.5447802Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:04:26.5448011Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:04:26.5448108Z 2025-03-04T21:04:26.5448641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:26.5448843Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:26.5448998Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:04:26.5449085Z 2025-03-04T21:04:26.5449587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:26.5449824Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:26.5450010Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:04:26.5450097Z 2025-03-04T21:04:26.5450610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:26.5450842Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:26.5451019Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:04:26.5451110Z 2025-03-04T21:04:26.5451636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:26.5451929Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:04:26.5452150Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:04:26.5452241Z 2025-03-04T21:04:26.5452828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:26.5453057Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:04:26.5453157Z 2025-03-04T21:04:26.5453905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:26.5454181Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:04:26.5454274Z 2025-03-04T21:04:26.5454908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:26.5455139Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:04:26.5455355Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:04:26.5455619Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:04:26.5455854Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:04:26.5455948Z 2025-03-04T21:04:26.5456627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:26.5456867Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:04:26.5457085Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:04:26.5457345Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:04:26.5457587Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:04:26.5457689Z 2025-03-04T21:04:26.5458294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:26.5458489Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:04:26.5458765Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:04:26.5458996Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:04:26.5459088Z 2025-03-04T21:04:26.5459690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:26.5459862Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:04:26.5460156Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:04:26.5460370Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:04:26.5460470Z 2025-03-04T21:04:26.5461004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:26.5461166Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:04:26.5461362Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:04:26.5461459Z 2025-03-04T21:04:26.5461982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:26.5462178Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:04:26.5462369Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:04:26.5462500Z 2025-03-04T21:04:26.5463041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:26.5463240Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:04:26.5463492Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:04:26.5463616Z 2025-03-04T21:04:26.5464162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:26.5464350Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:04:26.5464572Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:04:26.5464670Z 2025-03-04T21:04:26.5465279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:26.5465594Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:04:26.5465708Z 2025-03-04T21:04:26.5466316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:26.5466594Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:04:26.5466692Z 2025-03-04T21:04:26.5467380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:26.5467679Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:04:26.5467766Z 2025-03-04T21:04:26.5468429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:26.5468776Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:04:26.5468879Z 2025-03-04T21:04:26.5469628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:26.5469879Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:04:26.5470128Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:04:26.5470351Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:04:26.5470450Z 2025-03-04T21:04:26.5471100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:26.5471382Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:04:26.5471473Z 2025-03-04T21:04:26.5472027Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:26.5472304Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:04:26.5472406Z 2025-03-04T21:04:26.5472955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:26.5473157Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:04:26.5473356Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:04:26.5473639Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:04:26.5473754Z 2025-03-04T21:04:26.5474313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:26.5474515Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:04:26.5474718Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:04:26.5474965Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:04:26.5475062Z 2025-03-04T21:04:26.5475611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:26.5475812Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:04:26.5475966Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:04:26.5476183Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:04:26.5476278Z 2025-03-04T21:04:26.5476838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:26.5477082Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:04:26.5477229Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:04:26.5477436Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:04:26.5477530Z 2025-03-04T21:04:26.5478075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:26.5478335Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:26.5478517Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:04:26.5478615Z 2025-03-04T21:04:26.5479140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:26.5479400Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:26.5479573Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:04:26.5479673Z 2025-03-04T21:04:26.5480198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:26.5480450Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:26.5480621Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:04:26.5480723Z 2025-03-04T21:04:26.5481262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:26.5481563Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:04:26.5481778Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:04:26.5481868Z 2025-03-04T21:04:26.5482467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:26.5482689Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:04:26.5482788Z 2025-03-04T21:04:26.5483400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:26.5483670Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:04:26.5483758Z 2025-03-04T21:04:26.5484381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:26.5484597Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:04:26.5484797Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:04:26.5485042Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:04:26.5485266Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:04:26.5485380Z 2025-03-04T21:04:26.5486006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:26.5486228Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:04:26.5486427Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:04:26.5486672Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:04:26.5486903Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:04:26.5486991Z 2025-03-04T21:04:26.5487582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:26.5487753Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:04:26.5488029Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:04:26.5488482Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:04:26.5488588Z 2025-03-04T21:04:26.5489150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:26.5489303Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:04:26.5489530Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:04:26.5489733Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:04:26.5489818Z 2025-03-04T21:04:26.5490346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:26.5490493Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:04:26.5490677Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:04:26.5490763Z 2025-03-04T21:04:26.5491427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:26.5491566Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:04:26.5491757Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:04:26.5491846Z 2025-03-04T21:04:26.5492380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:26.5492557Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:04:26.5492838Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:04:26.5492971Z 2025-03-04T21:04:26.5493513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:26.5493796Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:04:26.5494025Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:04:26.5494114Z 2025-03-04T21:04:26.5494772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:26.5495089Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:04:26.5495228Z 2025-03-04T21:04:26.5495831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:26.5496097Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:04:26.5496193Z 2025-03-04T21:04:26.5496876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:26.5497167Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:04:26.5497257Z 2025-03-04T21:04:26.5498128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:04:26.5498346Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:04:26.5498447Z 2025-03-04T21:04:26.5498968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5499202Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:04:26.5499288Z 2025-03-04T21:04:26.5500054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:26.5500224Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:04:26.5500390Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:04:26.5500561Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:04:26.5500661Z 2025-03-04T21:04:26.5501454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:26.5501666Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:26.5502068Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:04:26.5502163Z 2025-03-04T21:04:26.5502966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:26.5503274Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:26.5503365Z 2025-03-04T21:04:26.5503926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5504117Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:04:26.5504213Z 2025-03-04T21:04:26.5504981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:26.5505160Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:04:26.5505318Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:04:26.5505508Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:04:26.5505593Z 2025-03-04T21:04:26.5506483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:26.5506708Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:26.5507102Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:04:26.5507203Z 2025-03-04T21:04:26.5507991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:26.5508248Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:26.5508337Z 2025-03-04T21:04:26.5508853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5509059Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:04:26.5509150Z 2025-03-04T21:04:26.5509912Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:26.5510099Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:04:26.5510263Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:04:26.5510463Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:04:26.5510552Z 2025-03-04T21:04:26.5511371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:26.5511590Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:26.5512009Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:04:26.5512141Z 2025-03-04T21:04:26.5512975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:26.5513243Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:26.5513346Z 2025-03-04T21:04:26.5513904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5514139Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:04:26.5514228Z 2025-03-04T21:04:26.5515015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:26.5515200Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:04:26.5515374Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:04:26.5515556Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:04:26.5515653Z 2025-03-04T21:04:26.5516473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:26.5516738Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:26.5517151Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:04:26.5517240Z 2025-03-04T21:04:26.5518047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:26.5518319Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:26.5518415Z 2025-03-04T21:04:26.5518924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5519136Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:04:26.5519228Z 2025-03-04T21:04:26.5519998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:26.5520175Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:04:26.5520348Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:04:26.5520529Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:04:26.5520627Z 2025-03-04T21:04:26.5521448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:26.5521734Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:04:26.5522137Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:04:26.5522228Z 2025-03-04T21:04:26.5523058Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:26.5523338Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:26.5523427Z 2025-03-04T21:04:26.5523953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:26.5524194Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:04:26.5524320Z 2025-03-04T21:04:26.5524805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:26.5525474Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:04:26.5525564Z 2025-03-04T21:04:26.5526064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:26.5526876Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:04:26.5526997Z 2025-03-04T21:04:26.5527487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:26.5527813Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:04:26.5527908Z 2025-03-04T21:04:26.5528587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:04:26.5528820Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:04:26.5528898Z 2025-03-04T21:04:26.5529318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:04:26.5529548Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:04:26.5529644Z 2025-03-04T21:04:26.5530289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:04:26.5530506Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:04:26.5530592Z 2025-03-04T21:04:26.5531451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:04:26.5531666Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:04:26.5531856Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:04:26.5532103Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:04:26.5532321Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:04:26.5532408Z 2025-03-04T21:04:26.5533104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:04:26.5533285Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:04:26.5533384Z 2025-03-04T21:04:31.4623209Z 2025-03-04T21:04:31.4624081Z class GraphModule(torch.nn.Module): 2025-03-04T21:04:31.4626628Z def forward(self, L_pred_anchor_deltas_0_: "f32[4, 269952, 4][1079808, 4, 1]cpu", L_anchors_0_tensor: "f32[269952, 4][4, 1]cpu", L_pred_anchor_deltas_1_: "f32[4, 67488, 4][269952, 4, 1]cpu", L_anchors_1_tensor: "f32[67488, 4][4, 1]cpu", L_pred_anchor_deltas_2_: "f32[4, 16872, 4][67488, 4, 1]cpu", L_anchors_2_tensor: "f32[16872, 4][4, 1]cpu", L_pred_anchor_deltas_3_: "f32[4, 4218, 4][16872, 4, 1]cpu", L_anchors_3_tensor: "f32[4218, 4][4, 1]cpu", L_pred_anchor_deltas_4_: "f32[4, 1083, 4][4332, 4, 1]cpu", L_anchors_4_tensor: "f32[1083, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 269952][269952, 1]cpu", L_pred_objectness_logits_1_: "f32[4, 67488][67488, 1]cpu", L_pred_objectness_logits_2_: "f32[4, 16872][16872, 1]cpu", L_pred_objectness_logits_3_: "f32[4, 4218][4218, 1]cpu", L_pred_objectness_logits_4_: "f32[4, 1083][1083, 1]cpu"): 2025-03-04T21:04:31.4628577Z l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_ 2025-03-04T21:04:31.4628864Z l_anchors_0_tensor = L_anchors_0_tensor 2025-03-04T21:04:31.4629203Z l_pred_anchor_deltas_1_ = L_pred_anchor_deltas_1_ 2025-03-04T21:04:31.4629548Z l_anchors_1_tensor = L_anchors_1_tensor 2025-03-04T21:04:31.4629826Z l_pred_anchor_deltas_2_ = L_pred_anchor_deltas_2_ 2025-03-04T21:04:31.4630134Z l_anchors_2_tensor = L_anchors_2_tensor 2025-03-04T21:04:31.4630394Z l_pred_anchor_deltas_3_ = L_pred_anchor_deltas_3_ 2025-03-04T21:04:31.4630650Z l_anchors_3_tensor = L_anchors_3_tensor 2025-03-04T21:04:31.4630898Z l_pred_anchor_deltas_4_ = L_pred_anchor_deltas_4_ 2025-03-04T21:04:31.4631150Z l_anchors_4_tensor = L_anchors_4_tensor 2025-03-04T21:04:31.4631511Z l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_ 2025-03-04T21:04:31.4631916Z l_pred_objectness_logits_1_ = L_pred_objectness_logits_1_ 2025-03-04T21:04:31.4632285Z l_pred_objectness_logits_2_ = L_pred_objectness_logits_2_ 2025-03-04T21:04:31.4632605Z l_pred_objectness_logits_3_ = L_pred_objectness_logits_3_ 2025-03-04T21:04:31.4632895Z l_pred_objectness_logits_4_ = L_pred_objectness_logits_4_ 2025-03-04T21:04:31.4633146Z 2025-03-04T21:04:31.4633719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:31.4634449Z pred_anchor_deltas_i: "f32[1079808, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4); l_pred_anchor_deltas_0_ = None 2025-03-04T21:04:31.4634804Z 2025-03-04T21:04:31.4635423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:31.4636154Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0); l_anchors_0_tensor = None 2025-03-04T21:04:31.4636574Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:04:31.4636939Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:04:31.4637215Z 2025-03-04T21:04:31.4637709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:31.4638325Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.float(); pred_anchor_deltas_i = None 2025-03-04T21:04:31.4638621Z 2025-03-04T21:04:31.4639093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:31.4639647Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:04:31.4639916Z 2025-03-04T21:04:31.4640320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:31.4640828Z getitem: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:04:31.4641173Z getitem_1: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:31.4641531Z widths: "f32[1079808][1]cpu" = getitem - getitem_1; getitem = getitem_1 = None 2025-03-04T21:04:31.4641798Z 2025-03-04T21:04:31.4642205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:31.4642711Z getitem_2: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:04:31.4643020Z getitem_3: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:04:31.4643349Z heights: "f32[1079808][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:04:31.4643625Z 2025-03-04T21:04:31.4644023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:31.4644559Z getitem_4: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:31.4644825Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:04:31.4645098Z ctr_x: "f32[1079808][1]cpu" = getitem_4 + mul; getitem_4 = mul = None 2025-03-04T21:04:31.4645347Z 2025-03-04T21:04:31.4645755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:31.4646281Z getitem_5: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:04:31.4646581Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:04:31.4646860Z ctr_y: "f32[1079808][1]cpu" = getitem_5 + mul_1; getitem_5 = mul_1 = None 2025-03-04T21:04:31.4647108Z 2025-03-04T21:04:31.4647539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:31.4648058Z getitem_6: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:31.4648396Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_6 / 1.0; getitem_6 = None 2025-03-04T21:04:31.4648633Z 2025-03-04T21:04:31.4649027Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:31.4649532Z getitem_7: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:31.4649869Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_7 / 1.0; getitem_7 = None 2025-03-04T21:04:31.4650110Z 2025-03-04T21:04:31.4650502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:31.4651034Z getitem_8: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:31.4651360Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:04:31.4651595Z 2025-03-04T21:04:31.4651984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:31.4652562Z getitem_9: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:04:31.4652925Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:04:31.4653170Z 2025-03-04T21:04:31.4653777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:31.4654347Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:04:31.4654629Z 2025-03-04T21:04:31.4655200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:31.4655773Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:04:31.4656045Z 2025-03-04T21:04:31.4656500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:31.4657065Z getitem_10: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:04:31.4657402Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_10; dx = getitem_10 = None 2025-03-04T21:04:31.4657764Z getitem_11: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:04:31.4658169Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_11; mul_2 = getitem_11 = None 2025-03-04T21:04:31.4658450Z 2025-03-04T21:04:31.4658915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:31.4659500Z getitem_12: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:04:31.4659835Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_12; dy = getitem_12 = None 2025-03-04T21:04:31.4660181Z getitem_13: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:04:31.4660547Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_13; mul_3 = getitem_13 = None 2025-03-04T21:04:31.4660817Z 2025-03-04T21:04:31.4661257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:31.4661795Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:04:31.4662146Z getitem_14: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:04:31.4662513Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_14; exp = getitem_14 = None 2025-03-04T21:04:31.4662782Z 2025-03-04T21:04:31.4663219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:31.4663746Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:04:31.4664089Z getitem_15: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:04:31.4664467Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_15; exp_1 = getitem_15 = None 2025-03-04T21:04:31.4664740Z 2025-03-04T21:04:31.4665156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:31.4665645Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:04:31.4665927Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:04:31.4666172Z 2025-03-04T21:04:31.4666602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:31.4667091Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:04:31.4667359Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:04:31.4667599Z 2025-03-04T21:04:31.4667996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:31.4668507Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:04:31.4668828Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:04:31.4669084Z 2025-03-04T21:04:31.4669488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:31.4669971Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:04:31.4670274Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:04:31.4670529Z 2025-03-04T21:04:31.4670972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:31.4671572Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:04:31.4671916Z 2025-03-04T21:04:31.4672349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:31.4672909Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:04:31.4673204Z 2025-03-04T21:04:31.4673685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:31.4674294Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:04:31.4674596Z 2025-03-04T21:04:31.4675086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:31.4675765Z pred_anchor_deltas_i_1: "f32[269952, 4][4, 1]cpu" = l_pred_anchor_deltas_1_.reshape(-1, 4); l_pred_anchor_deltas_1_ = None 2025-03-04T21:04:31.4676096Z 2025-03-04T21:04:31.4676616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:31.4677297Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = l_anchors_1_tensor.unsqueeze(0); l_anchors_1_tensor = None 2025-03-04T21:04:31.4677699Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:04:31.4678053Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:04:31.4678322Z 2025-03-04T21:04:31.4678790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:31.4679391Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:04:31.4679686Z 2025-03-04T21:04:31.4680104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:31.4680730Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:04:31.4680999Z 2025-03-04T21:04:31.4681397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:31.4681896Z getitem_16: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:04:31.4682233Z getitem_17: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:04:31.4682593Z widths_1: "f32[269952][1]cpu" = getitem_16 - getitem_17; getitem_16 = getitem_17 = None 2025-03-04T21:04:31.4682860Z 2025-03-04T21:04:31.4683260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:31.4683757Z getitem_18: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:04:31.4684060Z getitem_19: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:04:31.4684380Z heights_1: "f32[269952][1]cpu" = getitem_18 - getitem_19; getitem_18 = getitem_19 = None 2025-03-04T21:04:31.4684651Z 2025-03-04T21:04:31.4685044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:31.4685548Z getitem_20: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:04:31.4685822Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:04:31.4686099Z ctr_x_1: "f32[269952][1]cpu" = getitem_20 + mul_10; getitem_20 = mul_10 = None 2025-03-04T21:04:31.4686355Z 2025-03-04T21:04:31.4686752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:31.4687268Z getitem_21: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:04:31.4687569Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:04:31.4687845Z ctr_y_1: "f32[269952][1]cpu" = getitem_21 + mul_11; getitem_21 = mul_11 = None 2025-03-04T21:04:31.4688349Z 2025-03-04T21:04:31.4688984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:31.4689665Z getitem_22: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:31.4689996Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_22 / 1.0; getitem_22 = None 2025-03-04T21:04:31.4690233Z 2025-03-04T21:04:31.4690620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:31.4691114Z getitem_23: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:31.4691441Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_23 / 1.0; getitem_23 = None 2025-03-04T21:04:31.4691678Z 2025-03-04T21:04:31.4692056Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:31.4692553Z getitem_24: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:31.4692870Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_24 / 1.0; getitem_24 = None 2025-03-04T21:04:31.4693103Z 2025-03-04T21:04:31.4693535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:31.4694142Z getitem_25: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:04:31.4694504Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_25 / 1.0; getitem_25 = None 2025-03-04T21:04:31.4694746Z 2025-03-04T21:04:31.4695203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:31.4695808Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:04:31.4696088Z 2025-03-04T21:04:31.4696553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:31.4697091Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:04:31.4697368Z 2025-03-04T21:04:31.4697801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:31.4698352Z getitem_26: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:04:31.4698680Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_26; dx_1 = getitem_26 = None 2025-03-04T21:04:31.4699029Z getitem_27: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:04:31.4699417Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_27; mul_12 = getitem_27 = None 2025-03-04T21:04:31.4699685Z 2025-03-04T21:04:31.4700151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:31.4700706Z getitem_28: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:04:31.4701033Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_28; dy_1 = getitem_28 = None 2025-03-04T21:04:31.4701373Z getitem_29: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:04:31.4701730Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_29; mul_13 = getitem_29 = None 2025-03-04T21:04:31.4701997Z 2025-03-04T21:04:31.4702422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:31.4702945Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:04:31.4703282Z getitem_30: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:04:31.4703643Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_30; exp_2 = getitem_30 = None 2025-03-04T21:04:31.4703906Z 2025-03-04T21:04:31.4704329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:31.4704838Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:04:31.4705180Z getitem_31: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:04:31.4705545Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_31; exp_3 = getitem_31 = None 2025-03-04T21:04:31.4705807Z 2025-03-04T21:04:31.4706211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:31.4706686Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:04:31.4706985Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:04:31.4707229Z 2025-03-04T21:04:31.4707625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:31.4708095Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:04:31.4708360Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:04:31.4708599Z 2025-03-04T21:04:31.4709010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:31.4709516Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:04:31.4709827Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:04:31.4710086Z 2025-03-04T21:04:31.4710485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:31.4710977Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:04:31.4711272Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:04:31.4711518Z 2025-03-04T21:04:31.4711946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:31.4712660Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:04:31.4712975Z 2025-03-04T21:04:31.4713395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:31.4713940Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:04:31.4714228Z 2025-03-04T21:04:31.4714690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:31.4715287Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:04:31.4715586Z 2025-03-04T21:04:31.4716060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:31.4716714Z pred_anchor_deltas_i_2: "f32[67488, 4][4, 1]cpu" = l_pred_anchor_deltas_2_.reshape(-1, 4); l_pred_anchor_deltas_2_ = None 2025-03-04T21:04:31.4717040Z 2025-03-04T21:04:31.4717542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:31.4718194Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = l_anchors_2_tensor.unsqueeze(0); l_anchors_2_tensor = None 2025-03-04T21:04:31.4718578Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:04:31.4718921Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:04:31.4719183Z 2025-03-04T21:04:31.4719631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:31.4720207Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_2.float(); pred_anchor_deltas_i_2 = None 2025-03-04T21:04:31.4720516Z 2025-03-04T21:04:31.4720907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:31.4721411Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:04:31.4721681Z 2025-03-04T21:04:31.4722084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:31.4722613Z getitem_32: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:04:31.4722954Z getitem_33: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:04:31.4723282Z widths_2: "f32[67488][1]cpu" = getitem_32 - getitem_33; getitem_32 = getitem_33 = None 2025-03-04T21:04:31.4723547Z 2025-03-04T21:04:31.4723946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:31.4724431Z getitem_34: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:04:31.4724728Z getitem_35: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:04:31.4725059Z heights_2: "f32[67488][1]cpu" = getitem_34 - getitem_35; getitem_34 = getitem_35 = None 2025-03-04T21:04:31.4725317Z 2025-03-04T21:04:31.4725735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:31.4726216Z getitem_36: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:04:31.4726482Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:04:31.4726755Z ctr_x_2: "f32[67488][1]cpu" = getitem_36 + mul_20; getitem_36 = mul_20 = None 2025-03-04T21:04:31.4727013Z 2025-03-04T21:04:31.4727408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:31.4727912Z getitem_37: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:04:31.4728208Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:04:31.4728478Z ctr_y_2: "f32[67488][1]cpu" = getitem_37 + mul_21; getitem_37 = mul_21 = None 2025-03-04T21:04:31.4728730Z 2025-03-04T21:04:31.4729125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:31.4729632Z getitem_38: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:31.4729951Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_38 / 1.0; getitem_38 = None 2025-03-04T21:04:31.4730191Z 2025-03-04T21:04:31.4730573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:31.4731071Z getitem_39: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:31.4731391Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_39 / 1.0; getitem_39 = None 2025-03-04T21:04:31.4731626Z 2025-03-04T21:04:31.4732009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:31.4732503Z getitem_40: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:31.4732819Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_40 / 1.0; getitem_40 = None 2025-03-04T21:04:31.4733050Z 2025-03-04T21:04:31.4734374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:31.4734982Z getitem_41: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:04:31.4735344Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_41 / 1.0; getitem_41 = None 2025-03-04T21:04:31.4735590Z 2025-03-04T21:04:31.4736035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:31.4736621Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:04:31.4736927Z 2025-03-04T21:04:31.4737365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:31.4737916Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:04:31.4738194Z 2025-03-04T21:04:31.4738641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:31.4739198Z getitem_42: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:04:31.4739539Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_42; dx_2 = getitem_42 = None 2025-03-04T21:04:31.4739927Z getitem_43: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:04:31.4740287Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_43; mul_22 = getitem_43 = None 2025-03-04T21:04:31.4740558Z 2025-03-04T21:04:31.4741027Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:31.4741569Z getitem_44: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:04:31.4741893Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_44; dy_2 = getitem_44 = None 2025-03-04T21:04:31.4742228Z getitem_45: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:04:31.4742576Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_45; mul_23 = getitem_45 = None 2025-03-04T21:04:31.4742840Z 2025-03-04T21:04:31.4743268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:31.4743777Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:04:31.4744113Z getitem_46: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:04:31.4744467Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_46; exp_4 = getitem_46 = None 2025-03-04T21:04:31.4744732Z 2025-03-04T21:04:31.4745149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:31.4745654Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:04:31.4745992Z getitem_47: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:04:31.4746358Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_47; exp_5 = getitem_47 = None 2025-03-04T21:04:31.4746619Z 2025-03-04T21:04:31.4747024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:31.4747524Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:04:31.4747798Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:04:31.4748047Z 2025-03-04T21:04:31.4748446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:31.4748907Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:04:31.4749172Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:04:31.4749415Z 2025-03-04T21:04:31.4749834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:31.4750337Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:04:31.4750645Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:04:31.4750911Z 2025-03-04T21:04:31.4751294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:31.4751756Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:04:31.4752048Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:04:31.4752295Z 2025-03-04T21:04:31.4752726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:31.4753332Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:04:31.4753634Z 2025-03-04T21:04:31.4754049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:31.4754593Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:04:31.4754875Z 2025-03-04T21:04:31.4755336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:31.4755933Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:04:31.4756226Z 2025-03-04T21:04:31.4756701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:31.4757352Z pred_anchor_deltas_i_3: "f32[16872, 4][4, 1]cpu" = l_pred_anchor_deltas_3_.reshape(-1, 4); l_pred_anchor_deltas_3_ = None 2025-03-04T21:04:31.4757673Z 2025-03-04T21:04:31.4758180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:31.4758831Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = l_anchors_3_tensor.unsqueeze(0); l_anchors_3_tensor = None 2025-03-04T21:04:31.4759213Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:04:31.4759554Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:04:31.4759812Z 2025-03-04T21:04:31.4760258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:31.4760848Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:04:31.4761133Z 2025-03-04T21:04:31.4761517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:31.4762013Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:04:31.4762276Z 2025-03-04T21:04:31.4762683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:31.4763194Z getitem_48: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:04:31.4763503Z getitem_49: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:04:31.4763834Z widths_3: "f32[16872][1]cpu" = getitem_48 - getitem_49; getitem_48 = getitem_49 = None 2025-03-04T21:04:31.4764102Z 2025-03-04T21:04:31.4764502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:31.4764994Z getitem_50: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:04:31.4765293Z getitem_51: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:04:31.4765714Z heights_3: "f32[16872][1]cpu" = getitem_50 - getitem_51; getitem_50 = getitem_51 = None 2025-03-04T21:04:31.4765998Z 2025-03-04T21:04:31.4766392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:31.4766876Z getitem_52: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:04:31.4767148Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:04:31.4767441Z ctr_x_3: "f32[16872][1]cpu" = getitem_52 + mul_30; getitem_52 = mul_30 = None 2025-03-04T21:04:31.4767695Z 2025-03-04T21:04:31.4768092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:31.4768599Z getitem_53: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:04:31.4768895Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:04:31.4769172Z ctr_y_3: "f32[16872][1]cpu" = getitem_53 + mul_31; getitem_53 = mul_31 = None 2025-03-04T21:04:31.4769423Z 2025-03-04T21:04:31.4769813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:31.4770316Z getitem_54: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:31.4770639Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_54 / 1.0; getitem_54 = None 2025-03-04T21:04:31.4770876Z 2025-03-04T21:04:31.4771254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:31.4771754Z getitem_55: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:31.4772073Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_55 / 1.0; getitem_55 = None 2025-03-04T21:04:31.4772303Z 2025-03-04T21:04:31.4772681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:31.4773179Z getitem_56: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:31.4773590Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_56 / 1.0; getitem_56 = None 2025-03-04T21:04:31.4773868Z 2025-03-04T21:04:31.4774286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:31.4774861Z getitem_57: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:04:31.4775217Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_57 / 1.0; getitem_57 = None 2025-03-04T21:04:31.4775505Z 2025-03-04T21:04:31.4775976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:31.4776551Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:04:31.4776828Z 2025-03-04T21:04:31.4777264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:31.4777807Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:04:31.4778082Z 2025-03-04T21:04:31.4778531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:31.4779092Z getitem_58: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:04:31.4779449Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_58; dx_3 = getitem_58 = None 2025-03-04T21:04:31.4779803Z getitem_59: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:04:31.4780171Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_59; mul_32 = getitem_59 = None 2025-03-04T21:04:31.4780445Z 2025-03-04T21:04:31.4780895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:31.4781453Z getitem_60: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:04:31.4781788Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_60; dy_3 = getitem_60 = None 2025-03-04T21:04:31.4782147Z getitem_61: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:04:31.4782516Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_61; mul_33 = getitem_61 = None 2025-03-04T21:04:31.4782793Z 2025-03-04T21:04:31.4783224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:31.4783738Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:04:31.4784080Z getitem_62: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:04:31.4784447Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_62; exp_6 = getitem_62 = None 2025-03-04T21:04:31.4784727Z 2025-03-04T21:04:31.4785137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:31.4785624Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:04:31.4785954Z getitem_63: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:04:31.4786297Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_63; exp_7 = getitem_63 = None 2025-03-04T21:04:31.4786549Z 2025-03-04T21:04:31.4786961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:31.4787417Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:04:31.4787680Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:04:31.4787917Z 2025-03-04T21:04:31.4788633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:31.4789213Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:04:31.4789547Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:04:31.4789809Z 2025-03-04T21:04:31.4790200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:31.4790677Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:04:31.4790980Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:04:31.4791236Z 2025-03-04T21:04:31.4791621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:31.4792094Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:04:31.4792391Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:04:31.4792662Z 2025-03-04T21:04:31.4793087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:31.4793663Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:04:31.4793957Z 2025-03-04T21:04:31.4794367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:31.4794907Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:04:31.4795190Z 2025-03-04T21:04:31.4795649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:31.4796249Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:04:31.4796541Z 2025-03-04T21:04:31.4797017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:04:31.4797668Z pred_anchor_deltas_i_4: "f32[4332, 4][4, 1]cpu" = l_pred_anchor_deltas_4_.reshape(-1, 4); l_pred_anchor_deltas_4_ = None 2025-03-04T21:04:31.4797988Z 2025-03-04T21:04:31.4798498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:04:31.4799152Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = l_anchors_4_tensor.unsqueeze(0); l_anchors_4_tensor = None 2025-03-04T21:04:31.4799539Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:04:31.4799876Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:04:31.4800136Z 2025-03-04T21:04:31.4800612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:31.4801192Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_4.float(); pred_anchor_deltas_i_4 = None 2025-03-04T21:04:31.4801472Z 2025-03-04T21:04:31.4801858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:31.4802355Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:04:31.4802618Z 2025-03-04T21:04:31.4803134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:31.4803642Z getitem_64: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:04:31.4803947Z getitem_65: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:04:31.4804273Z widths_4: "f32[4332][1]cpu" = getitem_64 - getitem_65; getitem_64 = getitem_65 = None 2025-03-04T21:04:31.4804539Z 2025-03-04T21:04:31.4804937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:31.4805426Z getitem_66: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:04:31.4805724Z getitem_67: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:04:31.4806071Z heights_4: "f32[4332][1]cpu" = getitem_66 - getitem_67; getitem_66 = getitem_67 = None 2025-03-04T21:04:31.4806334Z 2025-03-04T21:04:31.4806727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:31.4807208Z getitem_68: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:04:31.4807476Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:04:31.4807745Z ctr_x_4: "f32[4332][1]cpu" = getitem_68 + mul_40; getitem_68 = mul_40 = None 2025-03-04T21:04:31.4807995Z 2025-03-04T21:04:31.4808389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:31.4808891Z getitem_69: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:04:31.4809182Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:04:31.4809454Z ctr_y_4: "f32[4332][1]cpu" = getitem_69 + mul_41; getitem_69 = mul_41 = None 2025-03-04T21:04:31.4809699Z 2025-03-04T21:04:31.4810097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:31.4810597Z getitem_70: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:31.4810913Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_70 / 1.0; getitem_70 = None 2025-03-04T21:04:31.4811143Z 2025-03-04T21:04:31.4811522Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:31.4812013Z getitem_71: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:31.4812329Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_71 / 1.0; getitem_71 = None 2025-03-04T21:04:31.4812565Z 2025-03-04T21:04:31.4812955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:31.4813536Z getitem_72: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:31.4813902Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_72 / 1.0; getitem_72 = None 2025-03-04T21:04:31.4814157Z 2025-03-04T21:04:31.4814581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:31.4815212Z getitem_73: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:04:31.4815586Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_73 / 1.0; getitem_73 = None 2025-03-04T21:04:31.4815840Z 2025-03-04T21:04:31.4816324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:31.4816915Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:04:31.4817197Z 2025-03-04T21:04:31.4817650Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:31.4818208Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:04:31.4818485Z 2025-03-04T21:04:31.4818945Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:31.4819519Z getitem_74: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:04:31.4819879Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_74; dx_4 = getitem_74 = None 2025-03-04T21:04:31.4820234Z getitem_75: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:04:31.4820607Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_75; mul_42 = getitem_75 = None 2025-03-04T21:04:31.4820884Z 2025-03-04T21:04:31.4821348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:31.4821918Z getitem_76: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:04:31.4822256Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_76; dy_4 = getitem_76 = None 2025-03-04T21:04:31.4822614Z getitem_77: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:04:31.4822984Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_77; mul_43 = getitem_77 = None 2025-03-04T21:04:31.4823261Z 2025-03-04T21:04:31.4823711Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:31.4824209Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:04:31.4824529Z getitem_78: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:04:31.4824865Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_78; exp_8 = getitem_78 = None 2025-03-04T21:04:31.4825113Z 2025-03-04T21:04:31.4825521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:31.4826016Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:04:31.4826340Z getitem_79: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:04:31.4826684Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_79; exp_9 = getitem_79 = None 2025-03-04T21:04:31.4826934Z 2025-03-04T21:04:31.4827362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:31.4827826Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:04:31.4828088Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:04:31.4828320Z 2025-03-04T21:04:31.4828708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:31.4829160Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:04:31.4829435Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:04:31.4829686Z 2025-03-04T21:04:31.4830071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:31.4830536Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:04:31.4830836Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:04:31.4831089Z 2025-03-04T21:04:31.4831478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:31.4831946Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:04:31.4832236Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:04:31.4832505Z 2025-03-04T21:04:31.4832933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:31.4833527Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:04:31.4833826Z 2025-03-04T21:04:31.4834238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:31.4834778Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:04:31.4835064Z 2025-03-04T21:04:31.4835540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:04:31.4836139Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:04:31.4836433Z 2025-03-04T21:04:31.4837006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:04:31.4837709Z arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:04:31.4837957Z 2025-03-04T21:04:31.4838346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:31.4838839Z batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:04:31.4839095Z 2025-03-04T21:04:31.4839628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:31.4840295Z topk = l_pred_objectness_logits_0_.topk(1000, dim = 1); l_pred_objectness_logits_0_ = None 2025-03-04T21:04:31.4840635Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:04:31.4840934Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:04:31.4841169Z 2025-03-04T21:04:31.4841716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:31.4842362Z getitem_82: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:31.4842812Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_82, topk_idx)]; proposals_i_5 = getitem_82 = topk_idx = None 2025-03-04T21:04:31.4843217Z 2025-03-04T21:04:31.4843761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:31.4844439Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:31.4844726Z 2025-03-04T21:04:31.4845112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:31.4845583Z to_6: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:04:31.4845825Z 2025-03-04T21:04:31.4846349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:31.4847033Z topk_1 = l_pred_objectness_logits_1_.topk(1000, dim = 1); l_pred_objectness_logits_1_ = None 2025-03-04T21:04:31.4847375Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:04:31.4847654Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:04:31.4847898Z 2025-03-04T21:04:31.4848444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:31.4849087Z getitem_86: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:31.4849515Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_86, topk_idx_1)]; proposals_i_6 = getitem_86 = topk_idx_1 = None 2025-03-04T21:04:31.4849871Z 2025-03-04T21:04:31.4850414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:31.4851088Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:31.4851378Z 2025-03-04T21:04:31.4851763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:31.4852244Z to_7: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:04:31.4852494Z 2025-03-04T21:04:31.4853012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:31.4853761Z topk_2 = l_pred_objectness_logits_2_.topk(1000, dim = 1); l_pred_objectness_logits_2_ = None 2025-03-04T21:04:31.4854121Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:04:31.4854417Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:04:31.4854668Z 2025-03-04T21:04:31.4855267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:31.4855910Z getitem_90: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:31.4856332Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_90, topk_idx_2)]; proposals_i_7 = getitem_90 = topk_idx_2 = None 2025-03-04T21:04:31.4856684Z 2025-03-04T21:04:31.4857247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:31.4857927Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:31.4858210Z 2025-03-04T21:04:31.4858587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:31.4859050Z to_8: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:04:31.4859292Z 2025-03-04T21:04:31.4859792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:31.4860448Z topk_3 = l_pred_objectness_logits_3_.topk(1000, dim = 1); l_pred_objectness_logits_3_ = None 2025-03-04T21:04:31.4860775Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:04:31.4861045Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:04:31.4861276Z 2025-03-04T21:04:31.4861805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:31.4862427Z getitem_94: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:04:31.4862832Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_94, topk_idx_3)]; proposals_i_8 = getitem_94 = topk_idx_3 = None 2025-03-04T21:04:31.4863172Z 2025-03-04T21:04:31.4863699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:31.4864352Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:31.4864629Z 2025-03-04T21:04:31.4865001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:31.4865464Z to_9: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:04:31.4865703Z 2025-03-04T21:04:31.4866208Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:04:31.4866848Z topk_4 = l_pred_objectness_logits_4_.topk(1000, dim = 1); l_pred_objectness_logits_4_ = None 2025-03-04T21:04:31.4867175Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:04:31.4867448Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:04:31.4867684Z 2025-03-04T21:04:31.4868235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:04:31.4868894Z getitem_98: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:04:31.4869331Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_98, topk_idx_4)]; proposals_i_9 = getitem_98 = topk_idx_4 = None 2025-03-04T21:04:31.4869676Z 2025-03-04T21:04:31.4870216Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:04:31.4870892Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:04:31.4871174Z 2025-03-04T21:04:31.4871552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:04:31.4872032Z to_10: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:04:31.4872281Z 2025-03-04T21:04:31.4872653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:31.4873373Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:04:31.4873891Z 2025-03-04T21:04:31.4874247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:31.4875048Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:04:31.4875628Z 2025-03-04T21:04:31.4875994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:31.4876517Z level_ids: "i64[5000][1]cpu" = torch.cat([to_6, to_7, to_8, to_9, to_10], 0); to_6 = to_7 = to_8 = to_9 = to_10 = level_ids = None 2025-03-04T21:04:31.4876832Z 2025-03-04T21:04:31.4877309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:04:31.4877895Z getitem_100: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:04:31.4878168Z 2025-03-04T21:04:31.4878557Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:04:31.4879056Z tensor: "f32[5000, 4][4, 1]cpu" = getitem_100.to(torch.float32); getitem_100 = None 2025-03-04T21:04:31.4879323Z 2025-03-04T21:04:31.4879790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:04:31.4880364Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:04:31.4880620Z 2025-03-04T21:04:31.4881194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:04:31.4881888Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor); tensor = None 2025-03-04T21:04:31.4882204Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:04:31.4882535Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:04:31.4882881Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:04:31.4883139Z 2025-03-04T21:04:31.4883615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:04:31.4884176Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:04:31.4884416Z 2025-03-04T21:04:57.4393737Z 2025-03-04T21:04:57.4398720Z class GraphModule(torch.nn.Module): 2025-03-04T21:04:57.4405422Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:04:57.4407927Z l_stack0_ = L_stack0_ 2025-03-04T21:04:57.4409718Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:04:57.4410374Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:04:57.4411503Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:04:57.4412051Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:04:57.4412633Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:04:57.4413224Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:04:57.4413808Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:04:57.4414380Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:04:57.4414877Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:04:57.4415401Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:04:57.4415849Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:04:57.4416270Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:04:57.4416584Z 2025-03-04T21:04:57.4417006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:04:57.4418256Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:04:57.4418998Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:04:57.4419880Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:04:57.4420685Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:04:57.4421523Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:04:57.4421816Z 2025-03-04T21:04:57.4422262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:04:57.4423258Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:04:57.4424017Z 2025-03-04T21:04:57.4424436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:04:57.4425466Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:04:57.4426215Z 2025-03-04T21:04:57.4426603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:04:57.4427081Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:57.4427347Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:04:57.4427591Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:04:57.4427882Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:57.4428140Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:04:57.4428389Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:04:57.4428672Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:57.4428968Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:04:57.4429211Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:04:57.4429485Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:57.4429738Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:04:57.4429975Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:04:57.4438792Z 2025-03-04T21:04:57.4439305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:57.4440152Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:04:57.4440735Z 2025-03-04T21:04:57.4441300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:57.4441902Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:04:57.4442189Z 2025-03-04T21:04:57.4442603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:57.4443147Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:04:57.4443471Z 2025-03-04T21:04:57.4443913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:57.4444429Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:04:57.4444758Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:57.4445102Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:04:57.4445383Z 2025-03-04T21:04:57.4445806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:57.4446319Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:04:57.4446675Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:04:57.4447025Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:04:57.4447306Z 2025-03-04T21:04:57.4447725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:57.4448230Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:57.4448517Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:04:57.4448798Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:04:57.4449058Z 2025-03-04T21:04:57.4449468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:57.4449999Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:04:57.4450320Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:04:57.4450614Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:04:57.4450867Z 2025-03-04T21:04:57.4451295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:57.4461596Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:57.4462116Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:04:57.4462380Z 2025-03-04T21:04:57.4462892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:57.4463468Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:57.4463814Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:04:57.4464060Z 2025-03-04T21:04:57.4464471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:57.4465124Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:57.4465471Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:04:57.4465721Z 2025-03-04T21:04:57.4466132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:57.4466695Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:04:57.4467137Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:04:57.4467411Z 2025-03-04T21:04:57.4467928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:57.4468465Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:04:57.4468718Z 2025-03-04T21:04:57.4469127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:57.4469634Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:04:57.4469883Z 2025-03-04T21:04:57.4470332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:57.4472040Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:04:57.4472381Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:04:57.4472736Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:04:57.4473090Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:04:57.4473356Z 2025-03-04T21:04:57.4473810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:57.4474363Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:04:57.4474695Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:04:57.4475035Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:04:57.4475402Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:04:57.4475661Z 2025-03-04T21:04:57.4476093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:57.4476611Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:04:57.4476947Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:04:57.4477293Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:04:57.4477550Z 2025-03-04T21:04:57.4477985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:57.4478497Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:04:57.4478839Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:04:57.4479228Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:04:57.4479488Z 2025-03-04T21:04:57.4479909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:57.4480369Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:04:57.4480633Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:04:57.4480868Z 2025-03-04T21:04:57.4481291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:57.4481784Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:04:57.4482051Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:04:57.4482342Z 2025-03-04T21:04:57.4482745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:57.4483228Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:04:57.4483529Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:04:57.4483782Z 2025-03-04T21:04:57.4484180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:57.4484732Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:04:57.4485031Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:04:57.4485284Z 2025-03-04T21:04:57.4485728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:57.4486326Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:04:57.4486624Z 2025-03-04T21:04:57.4487053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:57.4487628Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:04:57.4487923Z 2025-03-04T21:04:57.4488617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:04:57.4489314Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:04:57.4489750Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:04:57.4490047Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:04:57.4490351Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:04:57.4490664Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:04:57.4490933Z 2025-03-04T21:04:57.4491324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:04:57.4491898Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:04:57.4492250Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:04:57.4492493Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:04:57.4492861Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:04:57.4493290Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:04:57.4493538Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:04:57.4493906Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:04:57.4494253Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:04:57.4494485Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:04:57.4494891Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:04:57.4495353Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:04:57.4495611Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:04:57.4495837Z 2025-03-04T21:04:57.4496272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:04:57.4496848Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:04:57.4497145Z 2025-03-04T21:04:57.4497597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:04:57.4498269Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:04:57.4498727Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:04:57.4499020Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:04:57.4499317Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:04:57.4499622Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:04:57.4499880Z 2025-03-04T21:04:57.4500440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:04:57.4501140Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:04:57.4501481Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:04:57.4501819Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:04:57.4502160Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:04:57.4502452Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:04:57.4502694Z 2025-03-04T21:04:57.4503148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:04:57.4503689Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:04:57.4503930Z 2025-03-04T21:04:57.4504075Z 2025-03-04T21:04:57.4504173Z class GraphModule(torch.nn.Module): 2025-03-04T21:04:57.4506104Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:04:57.4508132Z l_stack0_ = L_stack0_ 2025-03-04T21:04:57.4508483Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:04:57.4508991Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:04:57.4509496Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:04:57.4509959Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:04:57.4510470Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:04:57.4511014Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:04:57.4511557Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:04:57.4512130Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:04:57.4512596Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:04:57.4512993Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:04:57.4513384Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:04:57.4513763Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:04:57.4514046Z 2025-03-04T21:04:57.4514407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:04:57.4514871Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:04:57.4515566Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:04:57.4516278Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:04:57.4516983Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:04:57.4517681Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:04:57.4517962Z 2025-03-04T21:04:57.4518363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:04:57.4519310Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:04:57.4520042Z 2025-03-04T21:04:57.4520468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:04:57.4521464Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:04:57.4522219Z 2025-03-04T21:04:57.4522613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:04:57.4523071Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:57.4523324Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:04:57.4523557Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:04:57.4523833Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:57.4524080Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:04:57.4524314Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:04:57.4524578Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:57.4524825Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:04:57.4525057Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:04:57.4525339Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:57.4525581Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:04:57.4525810Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:04:57.4526023Z 2025-03-04T21:04:57.4526388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:57.4527149Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:04:57.4527689Z 2025-03-04T21:04:57.4528144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:57.4528714Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:04:57.4528985Z 2025-03-04T21:04:57.4529380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:57.4529903Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:04:57.4530180Z 2025-03-04T21:04:57.4530577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:57.4531074Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:04:57.4531386Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:57.4531716Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:04:57.4531983Z 2025-03-04T21:04:57.4532387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:57.4532886Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:04:57.4533219Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:04:57.4533559Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:04:57.4533833Z 2025-03-04T21:04:57.4534227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:57.4534719Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:57.4535030Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:04:57.4535442Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:04:57.4535713Z 2025-03-04T21:04:57.4536150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:57.4536711Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:04:57.4537045Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:04:57.4537353Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:04:57.4537628Z 2025-03-04T21:04:57.4538043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:57.4538591Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:57.4538918Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:04:57.4539159Z 2025-03-04T21:04:57.4539548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:57.4540062Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:57.4540386Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:04:57.4540622Z 2025-03-04T21:04:57.4541011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:57.4541522Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:57.4541851Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:04:57.4542078Z 2025-03-04T21:04:57.4542468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:57.4543010Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:04:57.4543357Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:04:57.4543590Z 2025-03-04T21:04:57.4544016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:57.4544545Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:04:57.4544803Z 2025-03-04T21:04:57.4545227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:57.4545746Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:04:57.4546000Z 2025-03-04T21:04:57.4546453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:57.4547012Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:04:57.4547337Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:04:57.4547678Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:04:57.4548032Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:04:57.4548315Z 2025-03-04T21:04:57.4548775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:57.4549326Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:04:57.4549649Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:04:57.4549984Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:04:57.4550332Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:04:57.4550592Z 2025-03-04T21:04:57.4551021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:57.4551601Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:04:57.4551942Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:04:57.4552403Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:04:57.4552656Z 2025-03-04T21:04:57.4554042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:57.4554553Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:04:57.4554892Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:04:57.4555242Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:04:57.4555493Z 2025-03-04T21:04:57.4555895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:57.4556887Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:04:57.4557161Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:04:57.4557398Z 2025-03-04T21:04:57.4557804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:57.4558263Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:04:57.4558523Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:04:57.4558756Z 2025-03-04T21:04:57.4559149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:57.4559624Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:04:57.4559915Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:04:57.4560161Z 2025-03-04T21:04:57.4560554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:57.4561074Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:04:57.4561352Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:04:57.4561596Z 2025-03-04T21:04:57.4562024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:57.4562596Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:04:57.4562910Z 2025-03-04T21:04:57.4563347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:57.4563894Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:04:57.4564179Z 2025-03-04T21:04:57.4564630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:04:57.4565295Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:04:57.4565714Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:04:57.4565997Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:04:57.4566335Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:04:57.4566634Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:04:57.4566881Z 2025-03-04T21:04:57.4567256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:04:57.4567805Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:04:57.4568155Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:04:57.4568405Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:04:57.4568777Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:04:57.4569127Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:04:57.4569372Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:04:57.4569737Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:04:57.4570080Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:04:57.4570316Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:04:57.4570680Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:04:57.4571024Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:04:57.4571886Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:04:57.4572165Z 2025-03-04T21:04:57.4572619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:04:57.4573195Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:04:57.4573497Z 2025-03-04T21:04:57.4573949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:04:57.4574627Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:04:57.4575075Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:04:57.4575448Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:04:57.4575767Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:04:57.4576093Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:04:57.4576370Z 2025-03-04T21:04:57.4577030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:04:57.4577769Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:04:57.4578110Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:04:57.4578446Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:04:57.4578782Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:04:57.4579065Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:04:57.4579306Z 2025-03-04T21:04:57.4579746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:04:57.4580296Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:04:57.4580537Z 2025-03-04T21:04:57.4580676Z 2025-03-04T21:04:57.4580775Z class GraphModule(torch.nn.Module): 2025-03-04T21:04:57.4582669Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:04:57.4585215Z l_stack0_ = L_stack0_ 2025-03-04T21:04:57.4585577Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:04:57.4586059Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:04:57.4586542Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:04:57.4587009Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:04:57.4587530Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:04:57.4588272Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:04:57.4588856Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:04:57.4589514Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:04:57.4590000Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:04:57.4590412Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:04:57.4590812Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:04:57.4591241Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:04:57.4591571Z 2025-03-04T21:04:57.4591964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:04:57.4592443Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:04:57.4593157Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:04:57.4593887Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:04:57.4594611Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:04:57.4595349Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:04:57.4595627Z 2025-03-04T21:04:57.4596020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:04:57.4597019Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:04:57.4597714Z 2025-03-04T21:04:57.4598127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:04:57.4599109Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:04:57.4599821Z 2025-03-04T21:04:57.4600189Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:04:57.4600646Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:57.4600894Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:04:57.4601127Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:04:57.4601396Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:57.4601645Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:04:57.4601877Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:04:57.4602148Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:57.4602394Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:04:57.4602626Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:04:57.4602910Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:57.4603154Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:04:57.4603383Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:04:57.4603597Z 2025-03-04T21:04:57.4603964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:57.4604741Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:04:57.4605300Z 2025-03-04T21:04:57.4605754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:57.4606325Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:04:57.4606596Z 2025-03-04T21:04:57.4606990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:57.4607512Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:04:57.4607794Z 2025-03-04T21:04:57.4608190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:57.4609616Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:04:57.4609943Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:57.4610286Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:04:57.4610558Z 2025-03-04T21:04:57.4610977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:57.4611479Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:04:57.4611792Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:04:57.4612127Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:04:57.4612405Z 2025-03-04T21:04:57.4612800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:57.4613285Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:57.4613568Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:04:57.4613845Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:04:57.4614098Z 2025-03-04T21:04:57.4614509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:57.4615022Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:04:57.4615424Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:04:57.4615738Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:04:57.4616009Z 2025-03-04T21:04:57.4616449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:57.4617001Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:57.4617339Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:04:57.4617582Z 2025-03-04T21:04:57.4617980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:57.4618496Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:57.4618848Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:04:57.4619122Z 2025-03-04T21:04:57.4619516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:57.4620027Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:57.4620354Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:04:57.4620595Z 2025-03-04T21:04:57.4620994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:57.4621535Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:04:57.4621886Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:04:57.4622142Z 2025-03-04T21:04:57.4622573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:57.4623111Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:04:57.4623371Z 2025-03-04T21:04:57.4623796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:57.4624374Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:04:57.4624646Z 2025-03-04T21:04:57.4625080Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:57.4625640Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:04:57.4625967Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:04:57.4626306Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:04:57.4626660Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:04:57.4626922Z 2025-03-04T21:04:57.4627363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:57.4627915Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:04:57.4628234Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:04:57.4628573Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:04:57.4628926Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:04:57.4629186Z 2025-03-04T21:04:57.4629612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:57.4630149Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:04:57.4630486Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:04:57.4630846Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:04:57.4631114Z 2025-03-04T21:04:57.4631555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:57.4632102Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:04:57.4632463Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:04:57.4632820Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:04:57.4633076Z 2025-03-04T21:04:57.4633480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:57.4633948Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:04:57.4634211Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:04:57.4634448Z 2025-03-04T21:04:57.4634846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:57.4635351Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:04:57.4635609Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:04:57.4635842Z 2025-03-04T21:04:57.4636227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:57.4636696Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:04:57.4636983Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:04:57.4637222Z 2025-03-04T21:04:57.4637610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:57.4638087Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:04:57.4638380Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:04:57.4638632Z 2025-03-04T21:04:57.4639072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:57.4639668Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:04:57.4639969Z 2025-03-04T21:04:57.4640397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:57.4640966Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:04:57.4641255Z 2025-03-04T21:04:57.4641707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:04:57.4642398Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:04:57.4642832Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:04:57.4643122Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:04:57.4643451Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:04:57.4643753Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:04:57.4643999Z 2025-03-04T21:04:57.4644372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:04:57.4644920Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:04:57.4645283Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:04:57.4645545Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:04:57.4645911Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:04:57.4646255Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:04:57.4646495Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:04:57.4646854Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:04:57.4647195Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:04:57.4647434Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:04:57.4647790Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:04:57.4648122Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:04:57.4648373Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:04:57.4648590Z 2025-03-04T21:04:57.4649004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:04:57.4649547Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:04:57.4649826Z 2025-03-04T21:04:57.4650260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:04:57.4650906Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:04:57.4651314Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:04:57.4651596Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:04:57.4651885Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:04:57.4652181Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:04:57.4652422Z 2025-03-04T21:04:57.4652965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:04:57.4653645Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:04:57.4653976Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:04:57.4654292Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:04:57.4654628Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:04:57.4655603Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:04:57.4655875Z 2025-03-04T21:04:57.4656351Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:04:57.4657874Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:04:57.4658148Z 2025-03-04T21:04:57.4658289Z 2025-03-04T21:04:57.4658388Z class GraphModule(torch.nn.Module): 2025-03-04T21:04:57.4660337Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:04:57.4662369Z l_stack0_ = L_stack0_ 2025-03-04T21:04:57.4662722Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:04:57.4663224Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:04:57.4663727Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:04:57.4664195Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:04:57.4664713Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:04:57.4665271Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:04:57.4665827Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:04:57.4666435Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:04:57.4666931Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:04:57.4667338Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:04:57.4667736Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:04:57.4668131Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:04:57.4668424Z 2025-03-04T21:04:57.4668801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:04:57.4669274Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:04:57.4669979Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:04:57.4670704Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:04:57.4672531Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:04:57.4673589Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:04:57.4673875Z 2025-03-04T21:04:57.4674290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:04:57.4675293Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:04:57.4676023Z 2025-03-04T21:04:57.4676446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:04:57.4677460Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:04:57.4678194Z 2025-03-04T21:04:57.4678608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:04:57.4679081Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:57.4679351Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:04:57.4679581Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:04:57.4679852Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:57.4680097Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:04:57.4680335Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:04:57.4680606Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:57.4680849Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:04:57.4681082Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:04:57.4681343Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:57.4681591Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:04:57.4681819Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:04:57.4682040Z 2025-03-04T21:04:57.4682410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:57.4683176Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:04:57.4683717Z 2025-03-04T21:04:57.4684175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:57.4684744Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:04:57.4685016Z 2025-03-04T21:04:57.4685411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:57.4685941Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:04:57.4686219Z 2025-03-04T21:04:57.4686663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:57.4687163Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:04:57.4687476Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:57.4687803Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:04:57.4688206Z 2025-03-04T21:04:57.4688706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:57.4689249Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:04:57.4689574Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:04:57.4689925Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:04:57.4690206Z 2025-03-04T21:04:57.4690610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:57.4691106Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:57.4691395Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:04:57.4691724Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:04:57.4692033Z 2025-03-04T21:04:57.4692447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:57.4692972Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:04:57.4693284Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:04:57.4693572Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:04:57.4693830Z 2025-03-04T21:04:57.4694245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:57.4694768Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:57.4695105Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:04:57.4695401Z 2025-03-04T21:04:57.4695804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:57.4696328Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:57.4696655Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:04:57.4696892Z 2025-03-04T21:04:57.4697284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:57.4697792Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:57.4698115Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:04:57.4698359Z 2025-03-04T21:04:57.4698759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:57.4699302Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:04:57.4699654Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:04:57.4699922Z 2025-03-04T21:04:57.4700362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:57.4700908Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:04:57.4701170Z 2025-03-04T21:04:57.4701600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:57.4702151Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:04:57.4702428Z 2025-03-04T21:04:57.4702859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:57.4703421Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:04:57.4703747Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:04:57.4704091Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:04:57.4704442Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:04:57.4704703Z 2025-03-04T21:04:57.4705146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:57.4705716Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:04:57.4706040Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:04:57.4706377Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:04:57.4706732Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:04:57.4706986Z 2025-03-04T21:04:57.4707403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:57.4707893Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:04:57.4708220Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:04:57.4708561Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:04:57.4708805Z 2025-03-04T21:04:57.4709222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:57.4709727Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:04:57.4710058Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:04:57.4710401Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:04:57.4710649Z 2025-03-04T21:04:57.4711038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:57.4711497Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:04:57.4711761Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:04:57.4711994Z 2025-03-04T21:04:57.4712379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:57.4712863Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:04:57.4713123Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:04:57.4713355Z 2025-03-04T21:04:57.4713744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:57.4714212Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:04:57.4714514Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:04:57.4714781Z 2025-03-04T21:04:57.4715167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:57.4715635Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:04:57.4715926Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:04:57.4716172Z 2025-03-04T21:04:57.4716605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:57.4717184Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:04:57.4717474Z 2025-03-04T21:04:57.4717912Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:57.4718469Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:04:57.4718762Z 2025-03-04T21:04:57.4719209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:04:57.4719880Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:04:57.4720299Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:04:57.4720587Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:04:57.4720880Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:04:57.4721188Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:04:57.4721437Z 2025-03-04T21:04:57.4721813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:04:57.4722364Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:04:57.4722709Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:04:57.4722949Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:04:57.4723314Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:04:57.4723649Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:04:57.4723888Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:04:57.4724243Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:04:57.4724579Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:04:57.4724809Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:04:57.4725158Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:04:57.4725486Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:04:57.4725760Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:04:57.4725976Z 2025-03-04T21:04:57.4726387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:04:57.4726932Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:04:57.4727215Z 2025-03-04T21:04:57.4727666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:04:57.4728341Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:04:57.4728747Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:04:57.4729032Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:04:57.4729327Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:04:57.4729619Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:04:57.4729869Z 2025-03-04T21:04:57.4730408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:04:57.4731097Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:04:57.4731431Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:04:57.4731755Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:04:57.4732084Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:04:57.4732367Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:04:57.4732617Z 2025-03-04T21:04:57.4733054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:04:57.4733562Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:04:57.4733794Z 2025-03-04T21:04:59.2027554Z 2025-03-04T21:04:59.2033408Z class GraphModule(torch.nn.Module): 2025-03-04T21:04:59.2034887Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:04:59.2035922Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:04:59.2036223Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:04:59.2036562Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:04:59.2036996Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:04:59.2037417Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:04:59.2037837Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:04:59.2038151Z 2025-03-04T21:04:59.2039036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:04:59.2039572Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:59.2040155Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:04:59.2040406Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:04:59.2040701Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:59.2040969Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:04:59.2041219Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:04:59.2041510Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:59.2041772Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:04:59.2042069Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:04:59.2042397Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:59.2042652Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:04:59.2042891Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:04:59.2043123Z 2025-03-04T21:04:59.2043525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:59.2044354Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:04:59.2044946Z 2025-03-04T21:04:59.2045441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:59.2046092Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:04:59.2046376Z 2025-03-04T21:04:59.2046794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:59.2047356Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:04:59.2047652Z 2025-03-04T21:04:59.2048072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:59.2048605Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:04:59.2048938Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:59.2049289Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:04:59.2049570Z 2025-03-04T21:04:59.2050027Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:59.2050617Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:04:59.2050943Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:04:59.2051300Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:04:59.2051584Z 2025-03-04T21:04:59.2052000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:59.2052519Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:59.2052816Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:04:59.2053105Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:04:59.2053363Z 2025-03-04T21:04:59.2053801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:59.2054333Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:04:59.2054649Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:04:59.2054942Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:04:59.2055204Z 2025-03-04T21:04:59.2055824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:59.2056400Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:59.2056756Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:04:59.2057009Z 2025-03-04T21:04:59.2057436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:59.2057956Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:59.2058292Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:04:59.2058535Z 2025-03-04T21:04:59.2058937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:59.2059483Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:59.2059823Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:04:59.2060069Z 2025-03-04T21:04:59.2060491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:59.2061069Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:04:59.2061431Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:04:59.2061673Z 2025-03-04T21:04:59.2062126Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:59.2062700Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:04:59.2062967Z 2025-03-04T21:04:59.2063414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:59.2063944Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:04:59.2064205Z 2025-03-04T21:04:59.2064647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:59.2065206Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:04:59.2065539Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:04:59.2065891Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:04:59.2066256Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:04:59.2066527Z 2025-03-04T21:04:59.2066978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:59.2067551Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:04:59.2067877Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:04:59.2068217Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:04:59.2068568Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:04:59.2068831Z 2025-03-04T21:04:59.2069346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:59.2069875Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:04:59.2070214Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:04:59.2070562Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:04:59.2070819Z 2025-03-04T21:04:59.2071243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:59.2071747Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:04:59.2072087Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:04:59.2072440Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:04:59.2072721Z 2025-03-04T21:04:59.2073130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:59.2073598Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:04:59.2073875Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:04:59.2074123Z 2025-03-04T21:04:59.2074525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:59.2074989Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:04:59.2075253Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:04:59.2075489Z 2025-03-04T21:04:59.2075886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:59.2076368Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:04:59.2076663Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:04:59.2076912Z 2025-03-04T21:04:59.2077305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:59.2077776Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:04:59.2078071Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:04:59.2078318Z 2025-03-04T21:04:59.2078751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:59.2079335Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:04:59.2079627Z 2025-03-04T21:04:59.2080042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:59.2080620Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:04:59.2080913Z 2025-03-04T21:04:59.2081356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:04:59.2082033Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:04:59.2082458Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:04:59.2082767Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:04:59.2083108Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:04:59.2083418Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:04:59.2083675Z 2025-03-04T21:04:59.2084066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:04:59.2084635Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:04:59.2084986Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:04:59.2085235Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:04:59.2085609Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:04:59.2085977Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:04:59.2086219Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:04:59.2086585Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:04:59.2086930Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:04:59.2087169Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:04:59.2087531Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:04:59.2087873Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:04:59.2088316Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:04:59.2088553Z 2025-03-04T21:04:59.2088985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:04:59.2089600Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:04:59.2089961Z 2025-03-04T21:04:59.2090420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:04:59.2091098Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:04:59.2091517Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:04:59.2091810Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:04:59.2092117Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:04:59.2092432Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:04:59.2092692Z 2025-03-04T21:04:59.2093268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:04:59.2093988Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:04:59.2094393Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:04:59.2094746Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:04:59.2095100Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:04:59.2095458Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:04:59.2095707Z 2025-03-04T21:04:59.2096198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:04:59.2096832Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:04:59.2097074Z 2025-03-04T21:04:59.2112344Z 2025-03-04T21:04:59.2117208Z class GraphModule(torch.nn.Module): 2025-03-04T21:04:59.2122579Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:04:59.2123452Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:04:59.2123743Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:04:59.2124086Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:04:59.2124680Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:04:59.2125088Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:04:59.2125485Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:04:59.2125786Z 2025-03-04T21:04:59.2126209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:04:59.2126688Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:59.2126946Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:04:59.2127189Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:04:59.2127471Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:59.2127728Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:04:59.2127977Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:04:59.2128260Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:59.2128509Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:04:59.2128747Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:04:59.2129019Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:59.2129272Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:04:59.2129507Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:04:59.2129732Z 2025-03-04T21:04:59.2130120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:59.2130906Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:04:59.2131470Z 2025-03-04T21:04:59.2131939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:59.2132526Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:04:59.2132845Z 2025-03-04T21:04:59.2133264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:59.2133807Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:04:59.2134095Z 2025-03-04T21:04:59.2134507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:59.2135065Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:04:59.2135600Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:59.2135959Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:04:59.2136253Z 2025-03-04T21:04:59.2136673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:59.2137196Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:04:59.2137510Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:04:59.2137847Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:04:59.2138139Z 2025-03-04T21:04:59.2139123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:59.2139651Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:59.2139943Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:04:59.2140260Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:04:59.2140514Z 2025-03-04T21:04:59.2140918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:59.2141444Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:04:59.2141754Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:04:59.2142044Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:04:59.2142300Z 2025-03-04T21:04:59.2142722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:59.2143242Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:59.2143579Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:04:59.2143817Z 2025-03-04T21:04:59.2144211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:59.2144721Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:59.2145045Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:04:59.2145283Z 2025-03-04T21:04:59.2145674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:59.2146189Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:59.2146512Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:04:59.2146787Z 2025-03-04T21:04:59.2147199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:59.2147731Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:04:59.2148076Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:04:59.2148307Z 2025-03-04T21:04:59.2148758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:59.2150404Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:04:59.2150660Z 2025-03-04T21:04:59.2151075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:59.2151580Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:04:59.2151834Z 2025-03-04T21:04:59.2152259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:59.2152794Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:04:59.2153139Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:04:59.2153472Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:04:59.2153814Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:04:59.2154066Z 2025-03-04T21:04:59.2154498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:59.2155028Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:04:59.2155342Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:04:59.2155666Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:04:59.2156010Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:04:59.2156266Z 2025-03-04T21:04:59.2156678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:59.2157180Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:04:59.2157514Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:04:59.2157861Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:04:59.2158114Z 2025-03-04T21:04:59.2158543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:59.2159495Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:04:59.2159828Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:04:59.2160178Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:04:59.2160430Z 2025-03-04T21:04:59.2160860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:59.2161336Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:04:59.2161605Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:04:59.2161843Z 2025-03-04T21:04:59.2162248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:59.2162718Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:04:59.2163022Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:04:59.2163275Z 2025-03-04T21:04:59.2163663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:59.2164138Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:04:59.2164440Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:04:59.2164692Z 2025-03-04T21:04:59.2165092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:59.2165575Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:04:59.2165866Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:04:59.2166134Z 2025-03-04T21:04:59.2166575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:59.2167246Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:04:59.2167545Z 2025-03-04T21:04:59.2167978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:59.2168537Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:04:59.2168827Z 2025-03-04T21:04:59.2169329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:04:59.2170024Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:04:59.2170453Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:04:59.2170744Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:04:59.2171043Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:04:59.2171349Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:04:59.2171601Z 2025-03-04T21:04:59.2171980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:04:59.2172537Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:04:59.2172883Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:04:59.2173131Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:04:59.2173498Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:04:59.2173841Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:04:59.2174079Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:04:59.2174461Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:04:59.2174812Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:04:59.2175057Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:04:59.2175499Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:04:59.2175864Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:04:59.2176112Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:04:59.2176347Z 2025-03-04T21:04:59.2176822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:04:59.2177477Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:04:59.2177821Z 2025-03-04T21:04:59.2178283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:04:59.2178978Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:04:59.2179414Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:04:59.2179719Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:04:59.2180046Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:04:59.2180352Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:04:59.2180611Z 2025-03-04T21:04:59.2181180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:04:59.2181884Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:04:59.2182255Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:04:59.2182596Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:04:59.2182940Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:04:59.2183242Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:04:59.2183494Z 2025-03-04T21:04:59.2183956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:04:59.2184493Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:04:59.2184737Z 2025-03-04T21:04:59.2192186Z 2025-03-04T21:04:59.2192452Z class GraphModule(torch.nn.Module): 2025-03-04T21:04:59.2193505Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:04:59.2194405Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:04:59.2194648Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:04:59.2194979Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:04:59.2195401Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:04:59.2195972Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:04:59.2196392Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:04:59.2196702Z 2025-03-04T21:04:59.2197145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:04:59.2197639Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:59.2197908Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:04:59.2198194Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:04:59.2198517Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:59.2198776Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:04:59.2199026Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:04:59.2199310Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:59.2199572Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:04:59.2199813Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:04:59.2200091Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:04:59.2200355Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:04:59.2200585Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:04:59.2200805Z 2025-03-04T21:04:59.2201187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:04:59.2202004Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:04:59.2202562Z 2025-03-04T21:04:59.2203038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:04:59.2203621Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:04:59.2203897Z 2025-03-04T21:04:59.2204304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:04:59.2204846Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:04:59.2205137Z 2025-03-04T21:04:59.2205545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:04:59.2206059Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:04:59.2206385Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:59.2206732Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:04:59.2206996Z 2025-03-04T21:04:59.2207402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:04:59.2207919Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:04:59.2208241Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:04:59.2208582Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:04:59.2208860Z 2025-03-04T21:04:59.2209262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:04:59.2209787Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:04:59.2210084Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:04:59.2210366Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:04:59.2210618Z 2025-03-04T21:04:59.2211022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:04:59.2211574Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:04:59.2211907Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:04:59.2212193Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:04:59.2212445Z 2025-03-04T21:04:59.2212863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:04:59.2213388Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:04:59.2213718Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:04:59.2213961Z 2025-03-04T21:04:59.2214354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:04:59.2214897Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:04:59.2216412Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:04:59.2216660Z 2025-03-04T21:04:59.2217083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:04:59.2217618Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:04:59.2217963Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:04:59.2218212Z 2025-03-04T21:04:59.2218630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:04:59.2219206Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:04:59.2219578Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:04:59.2219825Z 2025-03-04T21:04:59.2220274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:04:59.2220839Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:04:59.2221115Z 2025-03-04T21:04:59.2221558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:04:59.2222108Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:04:59.2222377Z 2025-03-04T21:04:59.2222838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:04:59.2223419Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:04:59.2223758Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:04:59.2224174Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:04:59.2224556Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:04:59.2224832Z 2025-03-04T21:04:59.2225301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:04:59.2225884Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:04:59.2226253Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:04:59.2226626Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:04:59.2226991Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:04:59.2227252Z 2025-03-04T21:04:59.2227682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:04:59.2228190Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:04:59.2228523Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:04:59.2228876Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:04:59.2229160Z 2025-03-04T21:04:59.2229581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:04:59.2230083Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:04:59.2230422Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:04:59.2230775Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:04:59.2231025Z 2025-03-04T21:04:59.2231426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:04:59.2231892Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:04:59.2232157Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:04:59.2232397Z 2025-03-04T21:04:59.2232797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:04:59.2233256Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:04:59.2233515Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:04:59.2233743Z 2025-03-04T21:04:59.2234137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:04:59.2234611Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:04:59.2234905Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:04:59.2235154Z 2025-03-04T21:04:59.2235548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:04:59.2236026Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:04:59.2236318Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:04:59.2236567Z 2025-03-04T21:04:59.2237024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:04:59.2237608Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:04:59.2237927Z 2025-03-04T21:04:59.2238349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:04:59.2239678Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:04:59.2240013Z 2025-03-04T21:04:59.2240468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:04:59.2241166Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:04:59.2241590Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:04:59.2241876Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:04:59.2242175Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:04:59.2242480Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:04:59.2242734Z 2025-03-04T21:04:59.2243118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:04:59.2243698Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:04:59.2244046Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:04:59.2244291Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:04:59.2244660Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:04:59.2245006Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:04:59.2245244Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:04:59.2245608Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:04:59.2245946Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:04:59.2246183Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:04:59.2246547Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:04:59.2246889Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:04:59.2247121Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:04:59.2247342Z 2025-03-04T21:04:59.2247766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:04:59.2248371Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:04:59.2248697Z 2025-03-04T21:04:59.2249148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:04:59.2249815Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:04:59.2250231Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:04:59.2250516Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:04:59.2250810Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:04:59.2251133Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:04:59.2251387Z 2025-03-04T21:04:59.2251945Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:04:59.2252639Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:04:59.2252977Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:04:59.2253330Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:04:59.2253686Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:04:59.2253969Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:04:59.2254211Z 2025-03-04T21:04:59.2254659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:04:59.2255206Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:04:59.2255546Z 2025-03-04T21:05:02.7069347Z 2025-03-04T21:05:02.7071991Z class GraphModule(torch.nn.Module): 2025-03-04T21:05:02.7073906Z def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"): 2025-03-04T21:05:02.7075158Z l_scores_0_ = L_scores_0_ 2025-03-04T21:05:02.7080022Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:05:02.7083979Z 2025-03-04T21:05:02.7089003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:05:02.7090077Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:05:02.7090434Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:05:02.7090763Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:05:02.7091111Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:05:02.7091409Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:05:02.7091656Z 2025-03-04T21:05:02.7092139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:05:02.7092678Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:05:02.7092914Z 2025-03-04T21:05:02.7093011Z 2025-03-04T21:05:02.7093103Z class GraphModule(torch.nn.Module): 2025-03-04T21:05:02.7093447Z def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"): 2025-03-04T21:05:02.7093774Z l_scores_0_ = L_scores_0_ 2025-03-04T21:05:02.7093986Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:05:02.7094182Z 2025-03-04T21:05:02.7094745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:05:02.7095537Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:05:02.7095874Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:05:02.7096200Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:05:02.7096530Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:05:02.7097043Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:05:02.7097309Z 2025-03-04T21:05:02.7097759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:05:02.7098282Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:05:02.7098518Z 2025-03-04T21:05:22.3945898Z Compilation time (from dynamo_timed): 83.477260811 2025-03-04T21:05:22.3951129Z pass 2025-03-04T21:05:22.3953998Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:05:22.3955152Z TIMING: entire_frame_compile:83.47726 gc:0.05051 _recursive_pre_grad_passes:0.04093 async_compile.wait:31.75312 backend_compile:62.15691 _recursive_joint_graph_passes:0.31476 _recursive_post_grad_passes:0.23406 code_gen:40.40912 inductor_compile:45.45191 total_wall_time:83.47726 2025-03-04T21:05:22.3960509Z STATS: call_* op count: 1160 | FakeTensorMode.__torch_dispatch__:42548 | FakeTensor.__torch_dispatch__:4400 | ProxyTorchDispatchMode.__torch_dispatch__:13986 | attempt fast:202 | slow no contiguity match:72 | fast is_contiguous:130 2025-03-04T21:05:22.3965212Z Dynamo produced 61 graphs covering 1160 ops with 46 graph breaks (6 unique) 2025-03-04T21:05:29.5504063Z 2025-03-04T21:05:41.2215936Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:05:41.2218333Z loading model: 0it [00:11, ?it/s] 2025-03-04T21:05:41.2223902Z cpu eval detectron2_fasterrcnn_r_50_c4 2025-03-04T21:05:48.3160399Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_50_c4. Setting accuracy check to cosine 2025-03-04T21:05:48.3214183Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:06:07.1373927Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:06:25.1298982Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:06:33.8895044Z 2025-03-04T21:06:33.8900317Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:33.8947978Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", 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1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:06:33.8989536Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:06:33.8989986Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.8990664Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.8991388Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.8992087Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.8992754Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.8993416Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.8994110Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.8994867Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.8995586Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.8996339Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.8996995Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.8997680Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.8998452Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.8999189Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.8999876Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9000527Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9001205Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9001975Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9002687Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9003380Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9004050Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:33.9004766Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9005534Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9006280Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9007000Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9007670Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9008354Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9009096Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9009827Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9010514Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9011156Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9011855Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9012608Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9013314Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9014070Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9014734Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9015449Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9016184Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9016889Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9017571Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9018217Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9018899Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9019625Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9020333Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9021015Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9021664Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9022349Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9023078Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9023813Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9024492Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9025133Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9025820Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9026600Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9027331Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9028016Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9028668Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9029367Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9030097Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9030806Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9031493Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9032136Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9032817Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9033541Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9034245Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9034930Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9035578Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9036254Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9036994Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9037701Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9038391Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9039078Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:33.9039807Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9040566Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9041312Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9042028Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9042709Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9043391Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9044125Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9044825Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9045503Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9046148Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9046824Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9047547Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9048255Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9048937Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9049588Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9050293Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9051024Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9051727Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9052419Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9053092Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9053838Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9054580Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9055298Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9056001Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9056691Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9057385Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9058141Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9058872Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9059586Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9060276Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9060987Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9061747Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9062471Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9063178Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9063845Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9064574Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9065330Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9066062Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9066781Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9067459Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9068155Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9068898Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9069616Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9070340Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9070983Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9071665Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9072387Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9073088Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9073771Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9074412Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9075088Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9075812Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9076514Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9077189Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9077849Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9078521Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9079244Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9079966Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9080660Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9081310Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9081991Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9082722Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9083452Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9084137Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9084809Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:33.9085514Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9086272Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9087032Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9087761Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9088550Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9089261Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9090018Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9090752Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9091460Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9092186Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9092891Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9093737Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9094507Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9095227Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9095921Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9096646Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9097431Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9098213Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9098940Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9099621Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9100346Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9101114Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9101863Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9102579Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9103259Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9103956Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9104714Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9105450Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9106151Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9106803Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9107479Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9108221Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9108938Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9109617Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9110265Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9110940Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9111700Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9113160Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9113867Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9114516Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9115196Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9115924Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9116625Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9117315Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9117959Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9118643Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9119374Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9120077Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9120804Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9121456Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9122157Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9122913Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9123626Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9124315Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9124972Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9125655Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9126401Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9127107Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9127786Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9128445Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9129145Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9129886Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9130608Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9131308Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9131965Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9132657Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9133404Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9134206Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9134936Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9135635Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9136357Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9137128Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9137853Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9138551Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9139228Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9139975Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9140719Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9141443Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9142137Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9142865Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:06:33.9143606Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:06:33.9144310Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:06:33.9145058Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:06:33.9145845Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:06:33.9146627Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:06:33.9147408Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:06:33.9147887Z 2025-03-04T21:06:33.9148318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9149142Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9149759Z 2025-03-04T21:06:33.9150139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9151993Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9153601Z 2025-03-04T21:06:33.9153992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:06:33.9154514Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:06:33.9154794Z 2025-03-04T21:06:33.9155268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:06:33.9155927Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:06:33.9156286Z 2025-03-04T21:06:33.9156634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9157375Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9157913Z 2025-03-04T21:06:33.9158272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9160134Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9161763Z 2025-03-04T21:06:33.9162145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9162651Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:06:33.9162936Z 2025-03-04T21:06:33.9163286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9164032Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9164598Z 2025-03-04T21:06:33.9164987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9166874Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9168527Z 2025-03-04T21:06:33.9168916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9169421Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:06:33.9169690Z 2025-03-04T21:06:33.9170039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9170808Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9171379Z 2025-03-04T21:06:33.9171755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9173812Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9175530Z 2025-03-04T21:06:33.9175880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9176659Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:33.9177220Z 2025-03-04T21:06:33.9177579Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9179527Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9181255Z 2025-03-04T21:06:33.9181637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9182134Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:06:33.9182424Z 2025-03-04T21:06:33.9182806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9183310Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:06:33.9183588Z 2025-03-04T21:06:33.9183935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9184682Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9185225Z 2025-03-04T21:06:33.9185586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9187480Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9189264Z 2025-03-04T21:06:33.9189650Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9190149Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:06:33.9190423Z 2025-03-04T21:06:33.9190768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9191563Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9192120Z 2025-03-04T21:06:33.9192476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9194347Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9195990Z 2025-03-04T21:06:33.9196373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9196898Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:06:33.9197166Z 2025-03-04T21:06:33.9197507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9198255Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9198811Z 2025-03-04T21:06:33.9199163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9200973Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9202570Z 2025-03-04T21:06:33.9202932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9203416Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:06:33.9203690Z 2025-03-04T21:06:33.9204057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9204540Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:06:33.9204811Z 2025-03-04T21:06:33.9205163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9205885Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9206413Z 2025-03-04T21:06:33.9206785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9208622Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9210213Z 2025-03-04T21:06:33.9210586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9211067Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:06:33.9211326Z 2025-03-04T21:06:33.9211668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9212396Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9212935Z 2025-03-04T21:06:33.9213284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9215213Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9216813Z 2025-03-04T21:06:33.9217183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9217656Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:06:33.9217916Z 2025-03-04T21:06:33.9218243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9218994Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9219536Z 2025-03-04T21:06:33.9219893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9221733Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9223333Z 2025-03-04T21:06:33.9223697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9224203Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:06:33.9224474Z 2025-03-04T21:06:33.9224840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9225331Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:06:33.9225604Z 2025-03-04T21:06:33.9225935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9226658Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9227189Z 2025-03-04T21:06:33.9227537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9229327Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9230898Z 2025-03-04T21:06:33.9231262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9231745Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:06:33.9232022Z 2025-03-04T21:06:33.9232359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9233095Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9233640Z 2025-03-04T21:06:33.9234009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9235862Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9237459Z 2025-03-04T21:06:33.9237831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9238317Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:06:33.9238580Z 2025-03-04T21:06:33.9238918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9239679Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9240238Z 2025-03-04T21:06:33.9240605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9242468Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9244104Z 2025-03-04T21:06:33.9244456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9245243Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:33.9245815Z 2025-03-04T21:06:33.9246176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9248163Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9249860Z 2025-03-04T21:06:33.9250232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9250731Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:06:33.9251007Z 2025-03-04T21:06:33.9251403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9251904Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:06:33.9252187Z 2025-03-04T21:06:33.9252529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9253281Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9253931Z 2025-03-04T21:06:33.9254313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9256197Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9257796Z 2025-03-04T21:06:33.9258174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9258666Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:06:33.9258927Z 2025-03-04T21:06:33.9259257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9259999Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9260540Z 2025-03-04T21:06:33.9260887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9262691Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9264314Z 2025-03-04T21:06:33.9264681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9265180Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:06:33.9265446Z 2025-03-04T21:06:33.9265780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9266508Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9267052Z 2025-03-04T21:06:33.9267399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9269199Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9270795Z 2025-03-04T21:06:33.9271155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9271640Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:06:33.9271913Z 2025-03-04T21:06:33.9272281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9272769Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:06:33.9273038Z 2025-03-04T21:06:33.9273401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9274128Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9274660Z 2025-03-04T21:06:33.9275023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9276851Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9278460Z 2025-03-04T21:06:33.9278831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9279316Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:06:33.9279580Z 2025-03-04T21:06:33.9279909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9280650Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9281189Z 2025-03-04T21:06:33.9281537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9283376Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9284975Z 2025-03-04T21:06:33.9285343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9285824Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:06:33.9286087Z 2025-03-04T21:06:33.9286423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9287171Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9287706Z 2025-03-04T21:06:33.9288054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9291828Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9293682Z 2025-03-04T21:06:33.9294066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9294637Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:06:33.9294938Z 2025-03-04T21:06:33.9295321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9295825Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:06:33.9296110Z 2025-03-04T21:06:33.9296459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9297212Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9297767Z 2025-03-04T21:06:33.9298134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9300013Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9301641Z 2025-03-04T21:06:33.9302018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9302508Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:06:33.9302779Z 2025-03-04T21:06:33.9303149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9303901Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9304457Z 2025-03-04T21:06:33.9304833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9306707Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9308353Z 2025-03-04T21:06:33.9308728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9309210Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:06:33.9309475Z 2025-03-04T21:06:33.9309810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9310536Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9311078Z 2025-03-04T21:06:33.9311432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9313234Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9314825Z 2025-03-04T21:06:33.9315189Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9315675Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:06:33.9315946Z 2025-03-04T21:06:33.9316329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9316817Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:06:33.9317088Z 2025-03-04T21:06:33.9317420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9318159Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9318697Z 2025-03-04T21:06:33.9319048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9320887Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9322509Z 2025-03-04T21:06:33.9322876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9323352Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:06:33.9323607Z 2025-03-04T21:06:33.9323935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9324651Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9325180Z 2025-03-04T21:06:33.9325532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9327335Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9328918Z 2025-03-04T21:06:33.9329285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9329754Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:06:33.9330023Z 2025-03-04T21:06:33.9330357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9331080Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9331639Z 2025-03-04T21:06:33.9332020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9333917Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9335661Z 2025-03-04T21:06:33.9336010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9336763Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:33.9337325Z 2025-03-04T21:06:33.9337685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9339626Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9341312Z 2025-03-04T21:06:33.9341688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9342178Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:06:33.9342447Z 2025-03-04T21:06:33.9342826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9343323Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:06:33.9343596Z 2025-03-04T21:06:33.9343956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9344707Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9345246Z 2025-03-04T21:06:33.9345604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9347492Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9349114Z 2025-03-04T21:06:33.9349483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9349982Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:06:33.9350239Z 2025-03-04T21:06:33.9350575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9351293Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9351823Z 2025-03-04T21:06:33.9352170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9353966Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9355595Z 2025-03-04T21:06:33.9355973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9356460Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:06:33.9356727Z 2025-03-04T21:06:33.9357069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9357830Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9358381Z 2025-03-04T21:06:33.9358732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9360571Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9362217Z 2025-03-04T21:06:33.9362586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9363080Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:06:33.9363368Z 2025-03-04T21:06:33.9363734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9364212Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:06:33.9364472Z 2025-03-04T21:06:33.9364802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9365511Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9366038Z 2025-03-04T21:06:33.9366389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9368194Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9369772Z 2025-03-04T21:06:33.9370143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9370616Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:06:33.9370874Z 2025-03-04T21:06:33.9371229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9371958Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9372504Z 2025-03-04T21:06:33.9372867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9374954Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9376793Z 2025-03-04T21:06:33.9377168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9377715Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:06:33.9377996Z 2025-03-04T21:06:33.9378358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9379142Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9379715Z 2025-03-04T21:06:33.9380088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9381973Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9383584Z 2025-03-04T21:06:33.9384552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9385063Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:06:33.9385339Z 2025-03-04T21:06:33.9385719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9386239Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:06:33.9386510Z 2025-03-04T21:06:33.9386851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9387578Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9388298Z 2025-03-04T21:06:33.9388719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9391506Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9393194Z 2025-03-04T21:06:33.9393563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9394047Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:06:33.9394305Z 2025-03-04T21:06:33.9394640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9395383Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9395932Z 2025-03-04T21:06:33.9396292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9398138Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9399776Z 2025-03-04T21:06:33.9400162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9400657Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:06:33.9400927Z 2025-03-04T21:06:33.9401305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9402065Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9402625Z 2025-03-04T21:06:33.9402989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9404896Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9406612Z 2025-03-04T21:06:33.9406986Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9407484Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:06:33.9407753Z 2025-03-04T21:06:33.9408123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9408606Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:06:33.9408873Z 2025-03-04T21:06:33.9409215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9409949Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9410492Z 2025-03-04T21:06:33.9410850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9412710Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9414468Z 2025-03-04T21:06:33.9414896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9415456Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:06:33.9415762Z 2025-03-04T21:06:33.9416143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9416931Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9417534Z 2025-03-04T21:06:33.9417948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9419938Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9421685Z 2025-03-04T21:06:33.9422088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9422580Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:06:33.9422850Z 2025-03-04T21:06:33.9423214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9423994Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9424578Z 2025-03-04T21:06:33.9424951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9426914Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9428630Z 2025-03-04T21:06:33.9429022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9429543Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:06:33.9429828Z 2025-03-04T21:06:33.9430246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9430764Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:06:33.9431045Z 2025-03-04T21:06:33.9431407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9432194Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9432752Z 2025-03-04T21:06:33.9433116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9434979Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9436616Z 2025-03-04T21:06:33.9436993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9437477Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:06:33.9437741Z 2025-03-04T21:06:33.9438082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9438826Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9439378Z 2025-03-04T21:06:33.9439738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9441590Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9443222Z 2025-03-04T21:06:33.9443596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9444093Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:06:33.9444357Z 2025-03-04T21:06:33.9444695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9445434Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9445982Z 2025-03-04T21:06:33.9446353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9448221Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9449852Z 2025-03-04T21:06:33.9450223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9450709Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:06:33.9450976Z 2025-03-04T21:06:33.9451350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9451839Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:06:33.9452105Z 2025-03-04T21:06:33.9452634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:33.9453284Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:06:33.9453684Z 2025-03-04T21:06:33.9454134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:33.9454693Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:06:33.9454964Z 2025-03-04T21:06:33.9455503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:33.9456173Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:06:33.9456447Z 2025-03-04T21:06:33.9456834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:33.9457334Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:06:33.9457603Z 2025-03-04T21:06:33.9458091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:06:33.9458704Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:06:33.9459044Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:06:33.9459324Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:06:33.9459564Z 2025-03-04T21:06:33.9459991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:06:33.9460528Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:06:33.9460793Z 2025-03-04T21:06:33.9461213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:06:33.9461710Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:06:33.9461957Z 2025-03-04T21:06:33.9462431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:06:33.9463085Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:06:33.9463416Z 2025-03-04T21:06:33.9463930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:06:33.9464548Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:06:33.9465162Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:06:33.9465768Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:06:33.9466068Z x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:06:33.9466312Z 2025-03-04T21:06:33.9466715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:33.9467208Z tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32); x_88 = None 2025-03-04T21:06:33.9467463Z 2025-03-04T21:06:33.9467817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9468910Z x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:06:33.9469802Z 2025-03-04T21:06:33.9470172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:06:33.9470705Z x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False); x_89 = None 2025-03-04T21:06:33.9471017Z 2025-03-04T21:06:33.9471497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:06:33.9472868Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:06:33.9473830Z 2025-03-04T21:06:33.9474295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:06:33.9475555Z x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:06:33.9476491Z 2025-03-04T21:06:33.9476926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:06:33.9477499Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:06:33.9477867Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:06:33.9478131Z 2025-03-04T21:06:33.9478636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:06:33.9479260Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75); x_91 = None 2025-03-04T21:06:33.9479643Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:06:33.9480043Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:06:33.9480341Z 2025-03-04T21:06:33.9480837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:06:33.9481498Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:06:33.9481820Z 2025-03-04T21:06:33.9482343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:06:33.9482978Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:06:33.9483330Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:06:33.9483669Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:06:33.9483930Z 2025-03-04T21:06:33.9484396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:33.9484995Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:06:33.9485289Z 2025-03-04T21:06:33.9485719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:33.9486235Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:06:33.9486502Z 2025-03-04T21:06:33.9486908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:33.9487412Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:33.9487725Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:33.9488199Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:06:33.9488523Z 2025-03-04T21:06:33.9488940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:33.9489459Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:33.9489779Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:33.9490108Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:33.9490384Z 2025-03-04T21:06:33.9490794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:33.9491299Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:33.9491595Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:06:33.9491865Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:06:33.9492115Z 2025-03-04T21:06:33.9492520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:33.9493042Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:33.9493340Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:06:33.9493676Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:06:33.9493951Z 2025-03-04T21:06:33.9494413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:33.9494967Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:33.9495323Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:06:33.9495568Z 2025-03-04T21:06:33.9495969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:33.9496517Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:33.9496868Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:06:33.9497120Z 2025-03-04T21:06:33.9497536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:33.9498081Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:33.9498434Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:06:33.9498689Z 2025-03-04T21:06:33.9499107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:33.9499686Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:33.9500091Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:06:33.9500346Z 2025-03-04T21:06:33.9500804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:33.9501374Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:33.9501651Z 2025-03-04T21:06:33.9502125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:33.9502705Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:33.9502981Z 2025-03-04T21:06:33.9503443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:33.9504006Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:33.9504326Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:06:33.9504665Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:33.9505021Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:06:33.9505308Z 2025-03-04T21:06:33.9505749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:33.9506295Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:33.9506619Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:06:33.9506955Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:33.9507315Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:06:33.9507577Z 2025-03-04T21:06:33.9508000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:33.9508522Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:33.9508850Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:33.9509197Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:06:33.9509452Z 2025-03-04T21:06:33.9509871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:33.9510364Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:33.9510696Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:33.9511041Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:06:33.9511288Z 2025-03-04T21:06:33.9511687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:33.9512143Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:33.9512408Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:33.9512643Z 2025-03-04T21:06:33.9513056Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:33.9513513Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:33.9513775Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:33.9514011Z 2025-03-04T21:06:33.9514400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:33.9514879Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:33.9515205Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:33.9515475Z 2025-03-04T21:06:33.9515872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:33.9516349Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:33.9516652Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:33.9516897Z 2025-03-04T21:06:33.9517327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:33.9517901Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:33.9518214Z 2025-03-04T21:06:33.9518627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:33.9519167Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:06:33.9519445Z 2025-03-04T21:06:33.9519911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:06:33.9520509Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:06:33.9520796Z 2025-03-04T21:06:33.9521352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:06:33.9522020Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:06:33.9522271Z 2025-03-04T21:06:33.9522646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:33.9523127Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:06:33.9523388Z 2025-03-04T21:06:33.9523907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:06:33.9524497Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:06:33.9524769Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:06:33.9525040Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:06:33.9525273Z 2025-03-04T21:06:33.9525813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:06:33.9526480Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:06:33.9526950Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:06:33.9527295Z 2025-03-04T21:06:33.9527831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:06:33.9528495Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:06:33.9528800Z 2025-03-04T21:06:33.9529200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:33.9529699Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:06:33.9529969Z 2025-03-04T21:06:33.9530449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:06:33.9531041Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:06:33.9531314Z 2025-03-04T21:06:33.9531702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:33.9532240Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:06:33.9532512Z 2025-03-04T21:06:33.9532982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:06:33.9533622Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:06:33.9533908Z 2025-03-04T21:06:33.9534485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:06:33.9535190Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:06:33.9535528Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:33.9535890Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:06:33.9536260Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:06:33.9536536Z 2025-03-04T21:06:33.9537024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:06:33.9537584Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:06:33.9537835Z 2025-03-04T21:06:33.9538220Z 2025-03-04T21:06:33.9538316Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:33.9580709Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:06:33.9624986Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:06:33.9625477Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9626130Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9626840Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9627517Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9628244Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9628887Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9629574Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9630348Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9631096Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9631789Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9632469Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9633152Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9633932Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9634672Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9635387Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9636059Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9636774Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9637541Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9638272Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9638980Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9639668Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:33.9640406Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9641189Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9641962Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9642690Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9643371Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9644100Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9644862Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9645592Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9646293Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9646965Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9647682Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9648430Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9649160Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9666484Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9667204Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9667948Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9668714Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9669430Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9670119Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9670769Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9671458Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9672190Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9672996Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9673684Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9674336Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9675068Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9675862Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9676571Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9677256Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9677910Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9678611Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9679341Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9680040Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9680716Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9681361Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9682038Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9682765Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9683467Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9684148Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9684807Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9685485Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9686234Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9686940Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9687622Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9688537Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9689269Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9690020Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9690745Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9691440Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9692161Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:33.9692899Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9693762Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9694572Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9695325Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9696018Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9696711Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9697457Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9698176Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9698880Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9699549Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9700279Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9701025Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9701756Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9702474Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9703158Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9703855Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9704602Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9705323Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9706025Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9706757Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9707448Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9708192Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9708927Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9709621Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9710280Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9710983Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9711738Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9712446Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9713128Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9713772Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9714477Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9715207Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9715908Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9716602Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9717268Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9717950Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9718674Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9719385Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9720077Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9720726Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9721410Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9722139Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9722840Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9723521Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9724164Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9724833Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9725554Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9726251Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9726928Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9727582Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9728254Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9728974Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9729691Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9730393Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9731046Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9731726Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9732458Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9733188Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9734052Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9734818Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9735581Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9736443Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9737289Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9738100Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9738918Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:33.9739750Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9740634Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9741480Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9742219Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9742929Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9743626Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9744375Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9745116Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9745817Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9746484Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9747183Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9747935Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9748696Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9749413Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9750095Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9750822Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9751564Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9752287Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9752992Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9753660Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9754354Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9755096Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9755809Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9756525Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9757189Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9757887Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9758666Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9759600Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9760300Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9760963Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9761656Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9762443Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9763171Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9763870Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9764537Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9765225Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9765973Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9766690Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9767391Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9768036Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9768711Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9769448Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9769784Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9770099Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9770385Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9770751Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9771110Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9771451Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9771754Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9772046Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9772395Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9772752Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9773080Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9773394Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9773739Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9774087Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9774453Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9774834Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9775188Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9775484Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9775834Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9776187Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9776543Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9776878Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9777175Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:06:33.9777551Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9777914Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9778245Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9778571Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9778863Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:06:33.9779238Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9779584Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9779918Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9780226Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9780522Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:06:33.9780871Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:33.9781227Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:33.9781550Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:33.9781859Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:33.9782220Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:06:33.9782552Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:06:33.9782897Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:06:33.9783287Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:06:33.9783666Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:06:33.9784054Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:06:33.9784423Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:06:33.9784506Z 2025-03-04T21:06:33.9784827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9785326Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9785399Z 2025-03-04T21:06:33.9785705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9787255Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9787334Z 2025-03-04T21:06:33.9787648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:06:33.9787799Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:06:33.9787875Z 2025-03-04T21:06:33.9788397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:06:33.9788665Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:06:33.9788735Z 2025-03-04T21:06:33.9789013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9789455Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9789535Z 2025-03-04T21:06:33.9789815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9791487Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9791596Z 2025-03-04T21:06:33.9791907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9792061Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:06:33.9792129Z 2025-03-04T21:06:33.9792405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9792855Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9792977Z 2025-03-04T21:06:33.9793257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9794787Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9794861Z 2025-03-04T21:06:33.9795146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9795297Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:06:33.9795362Z 2025-03-04T21:06:33.9795621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9796057Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9796131Z 2025-03-04T21:06:33.9796402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9797970Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9798058Z 2025-03-04T21:06:33.9798319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9798775Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:33.9798839Z 2025-03-04T21:06:33.9799118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9800747Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9800842Z 2025-03-04T21:06:33.9801134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9801287Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:06:33.9801363Z 2025-03-04T21:06:33.9801653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9801817Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:06:33.9801882Z 2025-03-04T21:06:33.9802146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9802573Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9802646Z 2025-03-04T21:06:33.9802913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9804486Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9804561Z 2025-03-04T21:06:33.9804873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9805041Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:06:33.9805106Z 2025-03-04T21:06:33.9805366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9805797Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9805869Z 2025-03-04T21:06:33.9806134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9807664Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9807737Z 2025-03-04T21:06:33.9808015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9808164Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:06:33.9808228Z 2025-03-04T21:06:33.9808484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9808908Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9808983Z 2025-03-04T21:06:33.9809242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9810749Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9810824Z 2025-03-04T21:06:33.9811104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9811281Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:06:33.9811360Z 2025-03-04T21:06:33.9811645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9811792Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:06:33.9811864Z 2025-03-04T21:06:33.9812109Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9812528Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9812606Z 2025-03-04T21:06:33.9812877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9814707Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9814781Z 2025-03-04T21:06:33.9815078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9815231Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:06:33.9815302Z 2025-03-04T21:06:33.9815550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9815976Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9816040Z 2025-03-04T21:06:33.9816319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9817911Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9817979Z 2025-03-04T21:06:33.9818298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9818450Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:06:33.9818525Z 2025-03-04T21:06:33.9818781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9819229Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9819293Z 2025-03-04T21:06:33.9819573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9821121Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9821187Z 2025-03-04T21:06:33.9821472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9821625Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:06:33.9821698Z 2025-03-04T21:06:33.9821974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9822136Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:06:33.9822198Z 2025-03-04T21:06:33.9822454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9822880Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9822946Z 2025-03-04T21:06:33.9823210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9824740Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9824828Z 2025-03-04T21:06:33.9825107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9825260Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:06:33.9825324Z 2025-03-04T21:06:33.9825575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9826003Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9826086Z 2025-03-04T21:06:33.9826355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9827842Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9827915Z 2025-03-04T21:06:33.9828196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9828344Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:06:33.9828415Z 2025-03-04T21:06:33.9828662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9829089Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9829152Z 2025-03-04T21:06:33.9829421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9830925Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9830997Z 2025-03-04T21:06:33.9831264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9831714Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:33.9831784Z 2025-03-04T21:06:33.9832041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9833633Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9833724Z 2025-03-04T21:06:33.9834003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9834163Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:06:33.9834230Z 2025-03-04T21:06:33.9834522Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9834679Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:06:33.9834752Z 2025-03-04T21:06:33.9835006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9835442Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9835506Z 2025-03-04T21:06:33.9835775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9837311Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9837388Z 2025-03-04T21:06:33.9837696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9837857Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:06:33.9837927Z 2025-03-04T21:06:33.9838177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9838614Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9838678Z 2025-03-04T21:06:33.9838955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9840548Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9840646Z 2025-03-04T21:06:33.9840944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9841093Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:06:33.9841167Z 2025-03-04T21:06:33.9841423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9841865Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9841929Z 2025-03-04T21:06:33.9842203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9843764Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9843842Z 2025-03-04T21:06:33.9844131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9844307Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:06:33.9844428Z 2025-03-04T21:06:33.9844710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9844873Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:06:33.9844940Z 2025-03-04T21:06:33.9845196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9845619Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9845691Z 2025-03-04T21:06:33.9845978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9847518Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9847593Z 2025-03-04T21:06:33.9847881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9848031Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:06:33.9848095Z 2025-03-04T21:06:33.9848353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9848786Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9848860Z 2025-03-04T21:06:33.9849126Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9850679Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9850757Z 2025-03-04T21:06:33.9851062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9851224Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:06:33.9851288Z 2025-03-04T21:06:33.9851544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9851972Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9852042Z 2025-03-04T21:06:33.9852306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9853952Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9854042Z 2025-03-04T21:06:33.9854365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9854551Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:06:33.9854623Z 2025-03-04T21:06:33.9854944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9855104Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:06:33.9855179Z 2025-03-04T21:06:33.9855461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9855946Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9856021Z 2025-03-04T21:06:33.9856325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9858736Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9858834Z 2025-03-04T21:06:33.9859176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9859333Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:06:33.9859412Z 2025-03-04T21:06:33.9859684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9860163Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9860252Z 2025-03-04T21:06:33.9860553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9862223Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9862292Z 2025-03-04T21:06:33.9862590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9862736Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:06:33.9862811Z 2025-03-04T21:06:33.9863071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9863524Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9863597Z 2025-03-04T21:06:33.9863874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9865472Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9865542Z 2025-03-04T21:06:33.9865854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9866029Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:06:33.9866103Z 2025-03-04T21:06:33.9866397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9866563Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:06:33.9866630Z 2025-03-04T21:06:33.9866894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9867341Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9867437Z 2025-03-04T21:06:33.9867728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9869283Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9869360Z 2025-03-04T21:06:33.9869652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9869803Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:06:33.9869878Z 2025-03-04T21:06:33.9870136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9870576Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9870644Z 2025-03-04T21:06:33.9870921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9872522Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9872623Z 2025-03-04T21:06:33.9872906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9873042Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:06:33.9873112Z 2025-03-04T21:06:33.9873354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9873773Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9873852Z 2025-03-04T21:06:33.9874121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9875605Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9875680Z 2025-03-04T21:06:33.9875933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9876356Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:33.9876427Z 2025-03-04T21:06:33.9876686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9878270Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9878345Z 2025-03-04T21:06:33.9878628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9878779Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:06:33.9878859Z 2025-03-04T21:06:33.9879162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9879307Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:06:33.9879379Z 2025-03-04T21:06:33.9879629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9880053Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9880115Z 2025-03-04T21:06:33.9880380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9881867Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9881940Z 2025-03-04T21:06:33.9882231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9882368Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:06:33.9882437Z 2025-03-04T21:06:33.9882689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9883110Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9883175Z 2025-03-04T21:06:33.9883444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9885887Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9885983Z 2025-03-04T21:06:33.9886299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9886453Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:06:33.9886526Z 2025-03-04T21:06:33.9886777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9887212Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9887277Z 2025-03-04T21:06:33.9887549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9889173Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9889303Z 2025-03-04T21:06:33.9889592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9889747Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:06:33.9889823Z 2025-03-04T21:06:33.9890108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9890262Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:06:33.9890330Z 2025-03-04T21:06:33.9890593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9891006Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9891080Z 2025-03-04T21:06:33.9891346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9892897Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9892997Z 2025-03-04T21:06:33.9893320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9893480Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:06:33.9893551Z 2025-03-04T21:06:33.9893896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9894365Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9894448Z 2025-03-04T21:06:33.9894729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9896290Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9896363Z 2025-03-04T21:06:33.9896651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9896791Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:06:33.9896856Z 2025-03-04T21:06:33.9897113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9897534Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9897606Z 2025-03-04T21:06:33.9897872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9899434Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9899511Z 2025-03-04T21:06:33.9899812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9899984Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:06:33.9900049Z 2025-03-04T21:06:33.9900338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9900483Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:06:33.9900556Z 2025-03-04T21:06:33.9900807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9901232Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9901323Z 2025-03-04T21:06:33.9901596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9903115Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9903182Z 2025-03-04T21:06:33.9903472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9903605Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:06:33.9903678Z 2025-03-04T21:06:33.9903934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9904350Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9904415Z 2025-03-04T21:06:33.9904687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9906183Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9906262Z 2025-03-04T21:06:33.9906564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9906693Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:06:33.9906763Z 2025-03-04T21:06:33.9907009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9907430Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9907494Z 2025-03-04T21:06:33.9907767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9909279Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9909343Z 2025-03-04T21:06:33.9909628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9909769Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:06:33.9909839Z 2025-03-04T21:06:33.9910118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9910264Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:06:33.9910326Z 2025-03-04T21:06:33.9910579Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9910979Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9911049Z 2025-03-04T21:06:33.9911315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9912817Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9912904Z 2025-03-04T21:06:33.9913184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9913322Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:06:33.9913385Z 2025-03-04T21:06:33.9913636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9914049Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9914129Z 2025-03-04T21:06:33.9914407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9915876Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9915950Z 2025-03-04T21:06:33.9916235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9916370Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:06:33.9916434Z 2025-03-04T21:06:33.9916691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9917110Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9917174Z 2025-03-04T21:06:33.9917447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9918939Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9919033Z 2025-03-04T21:06:33.9919323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9919477Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:06:33.9919549Z 2025-03-04T21:06:33.9919828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9919975Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:06:33.9920038Z 2025-03-04T21:06:33.9920290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9920698Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:33.9920783Z 2025-03-04T21:06:33.9921041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9922520Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9922594Z 2025-03-04T21:06:33.9922874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9923011Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:06:33.9923074Z 2025-03-04T21:06:33.9923324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9923736Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:33.9923809Z 2025-03-04T21:06:33.9924066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9925571Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9925657Z 2025-03-04T21:06:33.9925938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9926076Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:06:33.9926142Z 2025-03-04T21:06:33.9926394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9926808Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:33.9926896Z 2025-03-04T21:06:33.9927156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:33.9928635Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:33.9928709Z 2025-03-04T21:06:33.9928981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:33.9929132Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:06:33.9929197Z 2025-03-04T21:06:33.9929479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:33.9929617Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:06:33.9929689Z 2025-03-04T21:06:33.9930117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:33.9930281Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:06:33.9930345Z 2025-03-04T21:06:33.9930642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:33.9930791Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:06:33.9930864Z 2025-03-04T21:06:33.9931302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:33.9931458Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:06:33.9931521Z 2025-03-04T21:06:33.9931834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:33.9931985Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:06:33.9932057Z 2025-03-04T21:06:33.9932427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:06:33.9932608Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:06:33.9932706Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:06:33.9932832Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:06:33.9932896Z 2025-03-04T21:06:33.9933232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:06:33.9933379Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:06:33.9933460Z 2025-03-04T21:06:33.9933900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:06:33.9934050Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:06:33.9934121Z 2025-03-04T21:06:33.9934567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:06:33.9934851Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:06:33.9934940Z 2025-03-04T21:06:33.9935459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:06:33.9935612Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:06:33.9936110Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:06:33.9936249Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:06:33.9936384Z x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:06:33.9936455Z 2025-03-04T21:06:33.9936813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:33.9936958Z tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32); x_88 = None 2025-03-04T21:06:33.9937040Z 2025-03-04T21:06:33.9937336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:33.9938232Z x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:06:33.9938305Z 2025-03-04T21:06:33.9938635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:06:33.9938887Z x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False); x_89 = None 2025-03-04T21:06:33.9938984Z 2025-03-04T21:06:33.9939426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:06:33.9940411Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:06:33.9940496Z 2025-03-04T21:06:33.9940938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:06:33.9941873Z x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:06:33.9941945Z 2025-03-04T21:06:33.9942359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:06:33.9942531Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:06:33.9942699Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:06:33.9942772Z 2025-03-04T21:06:33.9943266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:06:33.9943446Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75); x_91 = None 2025-03-04T21:06:33.9943652Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:06:33.9943859Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:06:33.9943931Z 2025-03-04T21:06:33.9944365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:06:33.9944576Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:06:33.9944647Z 2025-03-04T21:06:33.9945110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:06:33.9945270Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:06:33.9945417Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:06:33.9945562Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:06:33.9945627Z 2025-03-04T21:06:33.9946029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:33.9946213Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:06:33.9946285Z 2025-03-04T21:06:33.9946601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:33.9946748Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:06:33.9946813Z 2025-03-04T21:06:33.9947137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:33.9947268Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:33.9947422Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:33.9947568Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:06:33.9947641Z 2025-03-04T21:06:33.9947958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:33.9948091Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:33.9948210Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:33.9948364Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:33.9948429Z 2025-03-04T21:06:33.9948748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:33.9948872Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:33.9948973Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:06:33.9949096Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:06:33.9949170Z 2025-03-04T21:06:33.9949482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:33.9949633Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:33.9949723Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:06:33.9949860Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:06:33.9949925Z 2025-03-04T21:06:33.9950281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:33.9950435Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:33.9950559Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:06:33.9950623Z 2025-03-04T21:06:33.9950927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:33.9951091Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:33.9951213Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:06:33.9951276Z 2025-03-04T21:06:33.9951576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:33.9951731Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:33.9951891Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:06:33.9951998Z 2025-03-04T21:06:33.9952295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:33.9952484Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:33.9952593Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:06:33.9952662Z 2025-03-04T21:06:33.9952990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:33.9953134Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:33.9953211Z 2025-03-04T21:06:33.9953543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:33.9953677Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:33.9953747Z 2025-03-04T21:06:33.9954086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:33.9954229Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:33.9954352Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:06:33.9954510Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:33.9954649Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:06:33.9954723Z 2025-03-04T21:06:33.9955071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:33.9955217Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:33.9955342Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:06:33.9955501Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:33.9955637Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:06:33.9955709Z 2025-03-04T21:06:33.9956041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:33.9956171Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:33.9956332Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:33.9956472Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:06:33.9956537Z 2025-03-04T21:06:33.9956895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:33.9957014Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:33.9957189Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:33.9957322Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:06:33.9957394Z 2025-03-04T21:06:33.9957722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:33.9957843Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:33.9957964Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:33.9958036Z 2025-03-04T21:06:33.9958356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:33.9958456Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:33.9958569Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:33.9958641Z 2025-03-04T21:06:33.9958948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:33.9959087Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:33.9959234Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:33.9959296Z 2025-03-04T21:06:33.9959597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:33.9959708Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:33.9959844Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:33.9959907Z 2025-03-04T21:06:33.9960252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:33.9960428Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:33.9960499Z 2025-03-04T21:06:33.9960826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:33.9960990Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:06:33.9961053Z 2025-03-04T21:06:33.9961434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:06:33.9961601Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:06:33.9961673Z 2025-03-04T21:06:33.9962140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:06:33.9962279Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:06:33.9962343Z 2025-03-04T21:06:33.9962640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:33.9962793Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:06:33.9962866Z 2025-03-04T21:06:33.9963287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:06:33.9963409Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:06:33.9963513Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:06:33.9963654Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:06:33.9963732Z 2025-03-04T21:06:33.9964188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:06:33.9964347Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:06:33.9964585Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:06:33.9964649Z 2025-03-04T21:06:33.9965104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:06:33.9965290Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:06:33.9965363Z 2025-03-04T21:06:33.9965652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:33.9965807Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:06:33.9965871Z 2025-03-04T21:06:33.9966250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:06:33.9966402Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:06:33.9966466Z 2025-03-04T21:06:33.9966762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:33.9966905Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:06:33.9966976Z 2025-03-04T21:06:33.9967343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:06:33.9967484Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:06:33.9967546Z 2025-03-04T21:06:33.9968021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:06:33.9968154Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:06:33.9968280Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:33.9968438Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:06:33.9968576Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:06:33.9968640Z 2025-03-04T21:06:33.9969030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:06:33.9969146Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:06:33.9969217Z 2025-03-04T21:06:33.9969228Z 2025-03-04T21:06:33.9969319Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:34.0011175Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: 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L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:06:34.0014333Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:06:34.0014686Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:06:34.0015135Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0015564Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0015957Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0016259Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0016593Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:34.0016951Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0017288Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0017616Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0017925Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0018217Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:34.0018566Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0018904Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0019224Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0019532Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0019824Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:34.0020162Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0020502Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0020836Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0021154Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0021539Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:34.0021913Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0022283Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0022617Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0022945Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0023222Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:34.0023569Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0023905Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0024227Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0024543Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0024822Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:34.0025167Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0025501Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0025818Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0026122Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0026413Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:34.0026750Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0027098Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0027518Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0027928Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0028265Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:34.0028653Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0028989Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0029307Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0029619Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0029942Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:34.0030361Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0030774Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0031173Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0031562Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0031874Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:34.0032287Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0032697Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0033054Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0033395Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0033724Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:34.0034107Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0034503Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0034863Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0035225Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0035566Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:34.0035956Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0036337Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0036685Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0037036Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0037367Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:34.0037741Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0038121Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0038475Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0038844Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0039186Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:34.0039596Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0040004Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0040382Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0040753Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0041080Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:34.0041490Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0041861Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0042240Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0042609Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0042911Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:34.0043268Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0043638Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0043972Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0044275Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0044559Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:34.0044895Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0045233Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0045546Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0045860Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0046145Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:34.0046481Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0046815Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0047126Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0047435Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0047711Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:34.0048079Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0048429Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0048751Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0049079Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0049378Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:34.0049720Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0050054Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0050382Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0050690Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0050977Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:06:34.0051314Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0051654Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0051978Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0052288Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0052583Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:06:34.0052926Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0053273Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0053745Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0054120Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0054461Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:06:34.0054858Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0055227Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0055568Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0055906Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0056196Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:34.0056553Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0056896Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0057232Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0057548Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0057845Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:34.0058199Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0058539Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0058868Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0059181Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0059476Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:34.0059819Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0060171Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0060493Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0061576Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0062028Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:34.0062420Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0062799Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0063158Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0063501Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0063788Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:34.0064146Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0064487Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0064819Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0065138Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0065425Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:34.0065779Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0066121Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0066451Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0066764Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0067056Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:34.0067400Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0067749Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0068076Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0068402Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0068698Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:34.0069061Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0069420Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0069763Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0070077Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0070364Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:34.0070709Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0071052Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0071370Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0071687Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0071977Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:34.0072318Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0072643Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0072961Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0073264Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0073551Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:06:34.0073895Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0074222Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0074552Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0074862Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0075166Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:06:34.0075521Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0075873Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0076189Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0076504Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0076795Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:06:34.0077133Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0077472Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0077787Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0078099Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0078381Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:06:34.0078727Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0079057Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0079380Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0079694Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0079977Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:06:34.0080321Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0080652Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0080986Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0081308Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0081595Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:06:34.0081947Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0082298Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0082624Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0082930Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0083216Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:06:34.0083550Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0083887Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0084198Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0084510Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0084789Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:06:34.0085132Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0085469Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0085781Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0086091Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0086374Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:06:34.0086716Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:34.0087063Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:34.0087385Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:34.0087717Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:34.0088325Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:06:34.0088702Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:06:34.0089026Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:06:34.0089417Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:06:34.0089782Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:06:34.0090150Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:06:34.0090498Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:06:34.0090577Z 2025-03-04T21:06:34.0090861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0091354Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:34.0091423Z 2025-03-04T21:06:34.0091713Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0093191Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0093261Z 2025-03-04T21:06:34.0093666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:06:34.0093839Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:06:34.0093919Z 2025-03-04T21:06:34.0094415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:06:34.0094692Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:06:34.0094803Z 2025-03-04T21:06:34.0095070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0095527Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:34.0095614Z 2025-03-04T21:06:34.0095891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0097439Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0097515Z 2025-03-04T21:06:34.0097807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0097956Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:06:34.0098022Z 2025-03-04T21:06:34.0098284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0098717Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:34.0098782Z 2025-03-04T21:06:34.0099059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0100594Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0100671Z 2025-03-04T21:06:34.0100961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0101141Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:06:34.0101207Z 2025-03-04T21:06:34.0101467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0101933Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:34.0102012Z 2025-03-04T21:06:34.0102303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0103865Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0103941Z 2025-03-04T21:06:34.0104193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0104650Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:34.0104724Z 2025-03-04T21:06:34.0104988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0106605Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0106681Z 2025-03-04T21:06:34.0106965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:34.0107120Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:06:34.0107203Z 2025-03-04T21:06:34.0107491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0107651Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:06:34.0107715Z 2025-03-04T21:06:34.0107989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0108431Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:34.0108510Z 2025-03-04T21:06:34.0108792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0110297Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0110369Z 2025-03-04T21:06:34.0110653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0110801Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:06:34.0110863Z 2025-03-04T21:06:34.0111118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0111546Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:34.0111612Z 2025-03-04T21:06:34.0111877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0113363Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0113439Z 2025-03-04T21:06:34.0113727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0113869Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:06:34.0113938Z 2025-03-04T21:06:34.0114182Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0114629Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:34.0114709Z 2025-03-04T21:06:34.0114974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0116459Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0116547Z 2025-03-04T21:06:34.0116827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:34.0116983Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:06:34.0117055Z 2025-03-04T21:06:34.0117329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0117485Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:06:34.0117547Z 2025-03-04T21:06:34.0117796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0118221Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:34.0118293Z 2025-03-04T21:06:34.0118552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0120038Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0120112Z 2025-03-04T21:06:34.0120388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0120529Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:06:34.0120591Z 2025-03-04T21:06:34.0120996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0121438Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:34.0121512Z 2025-03-04T21:06:34.0121789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0123286Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0123361Z 2025-03-04T21:06:34.0123641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0123785Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:06:34.0123849Z 2025-03-04T21:06:34.0124103Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0124524Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:34.0124600Z 2025-03-04T21:06:34.0124861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0126355Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0126426Z 2025-03-04T21:06:34.0126701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:34.0126861Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:06:34.0126925Z 2025-03-04T21:06:34.0127222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0127375Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:06:34.0127488Z 2025-03-04T21:06:34.0127735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0128171Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:34.0128249Z 2025-03-04T21:06:34.0128516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0130009Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0130077Z 2025-03-04T21:06:34.0130369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0130515Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:06:34.0130587Z 2025-03-04T21:06:34.0130839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0131280Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:34.0131347Z 2025-03-04T21:06:34.0131623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0133153Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0133221Z 2025-03-04T21:06:34.0133539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0133773Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:06:34.0133877Z 2025-03-04T21:06:34.0134161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0134680Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:34.0134747Z 2025-03-04T21:06:34.0135059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0136664Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0136734Z 2025-03-04T21:06:34.0136995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0137439Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:34.0137514Z 2025-03-04T21:06:34.0137779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0139414Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0139494Z 2025-03-04T21:06:34.0139791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:34.0139955Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:06:34.0140024Z 2025-03-04T21:06:34.0140328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0140481Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:06:34.0140555Z 2025-03-04T21:06:34.0140824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0141262Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:34.0141344Z 2025-03-04T21:06:34.0141632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0143185Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0143251Z 2025-03-04T21:06:34.0143544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0143690Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:06:34.0143763Z 2025-03-04T21:06:34.0144014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0144457Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:34.0144522Z 2025-03-04T21:06:34.0144795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0146346Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0146415Z 2025-03-04T21:06:34.0146710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0146852Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:06:34.0146926Z 2025-03-04T21:06:34.0147172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0147634Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:34.0147718Z 2025-03-04T21:06:34.0147986Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0149535Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0149616Z 2025-03-04T21:06:34.0149902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:34.0150059Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:06:34.0150133Z 2025-03-04T21:06:34.0150413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0150572Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:06:34.0150635Z 2025-03-04T21:06:34.0150903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0151324Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:34.0151386Z 2025-03-04T21:06:34.0151649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0153142Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0153216Z 2025-03-04T21:06:34.0153492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0153636Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:06:34.0153705Z 2025-03-04T21:06:34.0153965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0154392Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:34.0154471Z 2025-03-04T21:06:34.0154752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0156244Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0156316Z 2025-03-04T21:06:34.0156601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0156739Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:06:34.0156809Z 2025-03-04T21:06:34.0157058Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0157484Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:34.0157549Z 2025-03-04T21:06:34.0157815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0159293Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0159367Z 2025-03-04T21:06:34.0159647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:34.0159799Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:06:34.0159870Z 2025-03-04T21:06:34.0160165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0160320Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:06:34.0160384Z 2025-03-04T21:06:34.0160650Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0161075Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:34.0161196Z 2025-03-04T21:06:34.0161451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0162937Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0163010Z 2025-03-04T21:06:34.0163287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0163432Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:06:34.0163494Z 2025-03-04T21:06:34.0163743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0164160Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:34.0164231Z 2025-03-04T21:06:34.0164487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0165976Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0166051Z 2025-03-04T21:06:34.0166327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0166468Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:06:34.0166550Z 2025-03-04T21:06:34.0166806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0167241Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:34.0167311Z 2025-03-04T21:06:34.0167584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0169096Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0169171Z 2025-03-04T21:06:34.0169452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:34.0170202Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:06:34.0170276Z 2025-03-04T21:06:34.0170776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0170929Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:06:34.0171005Z 2025-03-04T21:06:34.0172752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0173195Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:34.0173264Z 2025-03-04T21:06:34.0173545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0175255Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0175327Z 2025-03-04T21:06:34.0175660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0175804Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:06:34.0175897Z 2025-03-04T21:06:34.0176158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0176624Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:34.0176709Z 2025-03-04T21:06:34.0176990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0178558Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0178627Z 2025-03-04T21:06:34.0178928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0179070Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:06:34.0179141Z 2025-03-04T21:06:34.0179398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0180288Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:34.0180364Z 2025-03-04T21:06:34.0180651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0182258Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0182328Z 2025-03-04T21:06:34.0182598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0183068Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:34.0183163Z 2025-03-04T21:06:34.0183440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0185028Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0185118Z 2025-03-04T21:06:34.0185392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:34.0185539Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:06:34.0185604Z 2025-03-04T21:06:34.0185889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0186031Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:06:34.0186103Z 2025-03-04T21:06:34.0186350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0186765Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:34.0186827Z 2025-03-04T21:06:34.0187095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0188691Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0188762Z 2025-03-04T21:06:34.0189052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0189184Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:06:34.0189258Z 2025-03-04T21:06:34.0189550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0189970Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:34.0190062Z 2025-03-04T21:06:34.0190328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0191805Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0191896Z 2025-03-04T21:06:34.0192185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0192318Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:06:34.0192388Z 2025-03-04T21:06:34.0192631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0193050Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:34.0193125Z 2025-03-04T21:06:34.0193382Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0194843Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0194909Z 2025-03-04T21:06:34.0195189Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:34.0195334Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:06:34.0195408Z 2025-03-04T21:06:34.0195680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0195847Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:06:34.0195912Z 2025-03-04T21:06:34.0196163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0196584Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:34.0196647Z 2025-03-04T21:06:34.0196936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0198414Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0198488Z 2025-03-04T21:06:34.0198763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0198900Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:06:34.0198963Z 2025-03-04T21:06:34.0199218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0199632Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:34.0199696Z 2025-03-04T21:06:34.0199961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0201418Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0201491Z 2025-03-04T21:06:34.0201769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0201906Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:06:34.0201974Z 2025-03-04T21:06:34.0202232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0202651Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:34.0202727Z 2025-03-04T21:06:34.0202990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0204476Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0204562Z 2025-03-04T21:06:34.0204845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:34.0204991Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:06:34.0205065Z 2025-03-04T21:06:34.0205345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0205494Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:06:34.0205561Z 2025-03-04T21:06:34.0205815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0206222Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:34.0206295Z 2025-03-04T21:06:34.0206554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0208028Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0208103Z 2025-03-04T21:06:34.0208383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0208538Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:06:34.0208601Z 2025-03-04T21:06:34.0208851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0209284Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:34.0209355Z 2025-03-04T21:06:34.0209630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0211101Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0211174Z 2025-03-04T21:06:34.0211457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0211592Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:06:34.0211654Z 2025-03-04T21:06:34.0211911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0212321Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:34.0212393Z 2025-03-04T21:06:34.0212655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0214280Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0214364Z 2025-03-04T21:06:34.0214665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:34.0214833Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:06:34.0214898Z 2025-03-04T21:06:34.0215213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0215363Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:06:34.0215456Z 2025-03-04T21:06:34.0215721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0216185Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:34.0216267Z 2025-03-04T21:06:34.0216555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0218164Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0218236Z 2025-03-04T21:06:34.0218548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0218691Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:06:34.0218770Z 2025-03-04T21:06:34.0219037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0219486Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:34.0219557Z 2025-03-04T21:06:34.0220216Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0221847Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0221917Z 2025-03-04T21:06:34.0222227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0222389Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:06:34.0222467Z 2025-03-04T21:06:34.0222741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0223224Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:34.0223294Z 2025-03-04T21:06:34.0223604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0225163Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0225230Z 2025-03-04T21:06:34.0225508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:34.0225647Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:06:34.0225718Z 2025-03-04T21:06:34.0225993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0226140Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:06:34.0226202Z 2025-03-04T21:06:34.0226455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0226857Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:34.0226930Z 2025-03-04T21:06:34.0227187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0228653Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0228727Z 2025-03-04T21:06:34.0229018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0229157Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:06:34.0229233Z 2025-03-04T21:06:34.0229487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0229908Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:34.0229996Z 2025-03-04T21:06:34.0230254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0231734Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0231810Z 2025-03-04T21:06:34.0232087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0232225Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:06:34.0232286Z 2025-03-04T21:06:34.0232537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0232943Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:34.0233015Z 2025-03-04T21:06:34.0233271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:34.0234735Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:34.0234808Z 2025-03-04T21:06:34.0235079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:34.0235240Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:06:34.0235303Z 2025-03-04T21:06:34.0235583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:34.0235745Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:06:34.0235816Z 2025-03-04T21:06:34.0236258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:34.0236431Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:06:34.0236493Z 2025-03-04T21:06:34.0236791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:34.0236926Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:06:34.0236998Z 2025-03-04T21:06:34.0237429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:34.0237578Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:06:34.0237649Z 2025-03-04T21:06:34.0237937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:34.0238078Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:06:34.0238141Z 2025-03-04T21:06:34.0238517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:06:34.0238697Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:06:34.0238807Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:06:34.0238929Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:06:34.0239001Z 2025-03-04T21:06:34.0239334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:06:34.0239470Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:06:34.0239535Z 2025-03-04T21:06:34.0239871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:06:34.0239994Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:06:34.0240067Z 2025-03-04T21:06:34.0240448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:06:34.0240677Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:06:34.0240741Z 2025-03-04T21:06:34.0241163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:06:34.0241291Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:06:34.0241743Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:06:34.0241886Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:06:34.0242009Z x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:06:34.0242073Z 2025-03-04T21:06:34.0242396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:34.0242537Z tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32); x_88 = None 2025-03-04T21:06:34.0242612Z 2025-03-04T21:06:34.0242867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:34.0243642Z x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:06:34.0243714Z 2025-03-04T21:06:34.0243989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:06:34.0244182Z x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False); x_89 = None 2025-03-04T21:06:34.0244248Z 2025-03-04T21:06:34.0244639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:06:34.0245490Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:06:34.0245564Z 2025-03-04T21:06:34.0245924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:06:34.0246748Z x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:06:34.0246821Z 2025-03-04T21:06:34.0247157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:06:34.0247318Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:06:34.0247459Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:06:34.0247529Z 2025-03-04T21:06:34.0247968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:06:34.0248138Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75); x_91 = None 2025-03-04T21:06:34.0248328Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:06:34.0248511Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:06:34.0248577Z 2025-03-04T21:06:34.0249002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:06:34.0249224Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:06:34.0249300Z 2025-03-04T21:06:34.0249732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:06:34.0249890Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:06:34.0250039Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:06:34.0250182Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:06:34.0250247Z 2025-03-04T21:06:34.0250626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:34.0250798Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:06:34.0250872Z 2025-03-04T21:06:34.0251184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:34.0251331Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:06:34.0251397Z 2025-03-04T21:06:34.0251716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:34.0251852Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:34.0251980Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:34.0252132Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:06:34.0252197Z 2025-03-04T21:06:34.0252524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:34.0252646Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:34.0252770Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:34.0252912Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:34.0252981Z 2025-03-04T21:06:34.0253281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:34.0253406Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:34.0253500Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:06:34.0253728Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:06:34.0253806Z 2025-03-04T21:06:34.0254168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:34.0254323Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:34.0254447Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:06:34.0254585Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:06:34.0254661Z 2025-03-04T21:06:34.0255028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:34.0255207Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:34.0255336Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:06:34.0255408Z 2025-03-04T21:06:34.0255704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:34.0255862Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:34.0255977Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:06:34.0256049Z 2025-03-04T21:06:34.0256338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:34.0256498Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:34.0256609Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:06:34.0256680Z 2025-03-04T21:06:34.0256973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:34.0257163Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:34.0257271Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:06:34.0257346Z 2025-03-04T21:06:34.0257674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:34.0257821Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:34.0257885Z 2025-03-04T21:06:34.0258218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:34.0258352Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:34.0258424Z 2025-03-04T21:06:34.0258762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:34.0258907Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:34.0259040Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:06:34.0259189Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:34.0259333Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:06:34.0259398Z 2025-03-04T21:06:34.0260175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:34.0260320Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:34.0260469Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:06:34.0260618Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:34.0260780Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:06:34.0260845Z 2025-03-04T21:06:34.0261182Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:34.0261315Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:34.0261495Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:34.0261625Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:06:34.0261698Z 2025-03-04T21:06:34.0262022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:34.0262144Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:34.0262304Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:34.0262441Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:06:34.0262503Z 2025-03-04T21:06:34.0262821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:34.0262918Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:34.0263043Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:34.0263106Z 2025-03-04T21:06:34.0263415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:34.0263507Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:34.0263628Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:34.0263692Z 2025-03-04T21:06:34.0263995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:34.0264110Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:34.0264242Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:34.0264305Z 2025-03-04T21:06:34.0264603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:34.0264713Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:34.0264840Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:34.0264904Z 2025-03-04T21:06:34.0265250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:34.0265427Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:34.0265499Z 2025-03-04T21:06:34.0265823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:34.0265986Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:06:34.0266050Z 2025-03-04T21:06:34.0266444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:06:34.0266628Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:06:34.0266698Z 2025-03-04T21:06:34.0267219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:06:34.0267380Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:06:34.0267445Z 2025-03-04T21:06:34.0267741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:34.0267887Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:06:34.0267950Z 2025-03-04T21:06:34.0268381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:06:34.0268494Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:06:34.0268602Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:06:34.0268718Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:06:34.0268789Z 2025-03-04T21:06:34.0269239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:06:34.0269407Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:06:34.0269639Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:06:34.0269711Z 2025-03-04T21:06:34.0270157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:06:34.0270327Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:06:34.0270391Z 2025-03-04T21:06:34.0270691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:34.0270841Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:06:34.0270916Z 2025-03-04T21:06:34.0271293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:06:34.0271446Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:06:34.0271510Z 2025-03-04T21:06:34.0271812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:34.0271953Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:06:34.0272026Z 2025-03-04T21:06:34.0272398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:06:34.0272557Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:06:34.0272621Z 2025-03-04T21:06:34.0273101Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:06:34.0273255Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:06:34.0273396Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:34.0273566Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:06:34.0273705Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:06:34.0273768Z 2025-03-04T21:06:34.0274136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:06:34.0274250Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:06:34.0274323Z 2025-03-04T21:06:41.4250832Z 2025-03-04T21:06:41.4251559Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:41.4253096Z def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:06:41.4254703Z l_features_res4_ = L_features_res4_ 2025-03-04T21:06:41.4255145Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:06:41.4255713Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:06:41.4256230Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:06:41.4256801Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:06:41.4257425Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:06:41.4258026Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:06:41.4258605Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:06:41.4259002Z 2025-03-04T21:06:41.4259615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:41.4260315Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:06:41.4260608Z 2025-03-04T21:06:41.4261029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:41.4261552Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:06:41.4262115Z 2025-03-04T21:06:41.4262694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:41.4263439Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:06:41.4263727Z 2025-03-04T21:06:41.4264184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:41.4264726Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:06:41.4264986Z 2025-03-04T21:06:41.4265453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:06:41.4266067Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:06:41.4266404Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:06:41.4266673Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:06:41.4266916Z 2025-03-04T21:06:41.4267338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:06:41.4267853Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:06:41.4268102Z 2025-03-04T21:06:41.4268518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:06:41.4269022Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:06:41.4269261Z 2025-03-04T21:06:41.4269735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:06:41.4270386Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:06:41.4270712Z 2025-03-04T21:06:41.4271222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:06:41.4271816Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:06:41.4272313Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:06:41.4272799Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:06:41.4273089Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:06:41.4273323Z 2025-03-04T21:06:41.4273715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:41.4274189Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:06:41.4274430Z 2025-03-04T21:06:41.4274786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:41.4275730Z x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:06:41.4276438Z 2025-03-04T21:06:41.4276803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:06:41.4277348Z x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:06:41.4277653Z 2025-03-04T21:06:41.4278158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:06:41.4279264Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:06:41.4280016Z 2025-03-04T21:06:41.4280471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:06:41.4281482Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:06:41.4282203Z 2025-03-04T21:06:41.4282626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:06:41.4283172Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:06:41.4283518Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:06:41.4283785Z 2025-03-04T21:06:41.4284292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:06:41.4284917Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T21:06:41.4285306Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:06:41.4285710Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:06:41.4286007Z 2025-03-04T21:06:41.4286509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:06:41.4287177Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:06:41.4287500Z 2025-03-04T21:06:41.4288025Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:06:41.4288868Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:06:41.4289238Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:06:41.4289599Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:06:41.4289903Z 2025-03-04T21:06:41.4290376Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:41.4291011Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:06:41.4291305Z 2025-03-04T21:06:41.4291739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:41.4292271Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:06:41.4292534Z 2025-03-04T21:06:41.4292934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:41.4293503Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:41.4293837Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:41.4294178Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:06:41.4294451Z 2025-03-04T21:06:41.4294876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:41.4295389Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:41.4295706Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:41.4296039Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:41.4296317Z 2025-03-04T21:06:41.4296733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:41.4297242Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:41.4297522Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:06:41.4297797Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:06:41.4298048Z 2025-03-04T21:06:41.4298463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:41.4298998Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:41.4299308Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:06:41.4299592Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:06:41.4299851Z 2025-03-04T21:06:41.4300322Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:41.4300852Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:41.4301195Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:06:41.4301441Z 2025-03-04T21:06:41.4301848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:41.4302377Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:41.4302717Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:06:41.4302959Z 2025-03-04T21:06:41.4303367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:41.4303928Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:41.4304265Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:06:41.4304523Z 2025-03-04T21:06:41.4304926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:41.4305529Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:41.4305915Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:06:41.4306168Z 2025-03-04T21:06:41.4306587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:41.4307126Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:41.4307392Z 2025-03-04T21:06:41.4307819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:41.4308357Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:41.4308616Z 2025-03-04T21:06:41.4309062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:41.4309611Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:41.4309938Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:06:41.4310278Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:41.4310634Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:06:41.4310897Z 2025-03-04T21:06:41.4311339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:41.4311887Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:41.4312211Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:06:41.4312547Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:41.4312914Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:06:41.4313178Z 2025-03-04T21:06:41.4313605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:41.4314125Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:41.4314473Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:41.4314847Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:06:41.4315105Z 2025-03-04T21:06:41.4315545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:41.4316077Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:41.4316418Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:41.4316801Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:06:41.4317059Z 2025-03-04T21:06:41.4317464Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:41.4317948Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:41.4318218Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:41.4318461Z 2025-03-04T21:06:41.4318883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:41.4319365Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:41.4319633Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:41.4319878Z 2025-03-04T21:06:41.4320296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:41.4320773Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:41.4321075Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:41.4321328Z 2025-03-04T21:06:41.4321712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:41.4322194Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:41.4322486Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:41.4322736Z 2025-03-04T21:06:41.4323176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:41.4323769Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:41.4324062Z 2025-03-04T21:06:41.4324493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:41.4325044Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:06:41.4325325Z 2025-03-04T21:06:41.4325796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:06:41.4326403Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:06:41.4326696Z 2025-03-04T21:06:41.4327267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:06:41.4327946Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:06:41.4328203Z 2025-03-04T21:06:41.4328591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:41.4329088Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:06:41.4329353Z 2025-03-04T21:06:41.4329884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:06:41.4330493Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:06:41.4330789Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:06:41.4331065Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:06:41.4331303Z 2025-03-04T21:06:41.4331883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:06:41.4332562Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:06:41.4333055Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:06:41.4333500Z 2025-03-04T21:06:41.4334072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:06:41.4334762Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:06:41.4335068Z 2025-03-04T21:06:41.4335483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:41.4336024Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:06:41.4336316Z 2025-03-04T21:06:41.4336837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:06:41.4337444Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:06:41.4337727Z 2025-03-04T21:06:41.4338132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:41.4338648Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:06:41.4338924Z 2025-03-04T21:06:41.4339409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:06:41.4340000Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:06:41.4340270Z 2025-03-04T21:06:41.4340865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:06:41.4341562Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:06:41.4341887Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:41.4342225Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:06:41.4342583Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:06:41.4342846Z 2025-03-04T21:06:41.4343317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:06:41.4343873Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:06:41.4344120Z 2025-03-04T21:06:41.4344275Z 2025-03-04T21:06:41.4344370Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:41.4345747Z def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:06:41.4347105Z l_features_res4_ = L_features_res4_ 2025-03-04T21:06:41.4347535Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:06:41.4348074Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:06:41.4348569Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:06:41.4349117Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:06:41.4349714Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:06:41.4350298Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:06:41.4350864Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:06:41.4351227Z 2025-03-04T21:06:41.4351767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:41.4352410Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:06:41.4352686Z 2025-03-04T21:06:41.4353067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:41.4353566Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:06:41.4353826Z 2025-03-04T21:06:41.4354357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:41.4355000Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:06:41.4355271Z 2025-03-04T21:06:41.4355661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:41.4356152Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:06:41.4356415Z 2025-03-04T21:06:41.4356878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:06:41.4357495Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:06:41.4357836Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:06:41.4358109Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:06:41.4358347Z 2025-03-04T21:06:41.4358792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:06:41.4359313Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:06:41.4359565Z 2025-03-04T21:06:41.4360009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:06:41.4360510Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:06:41.4360754Z 2025-03-04T21:06:41.4361245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:06:41.4361911Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:06:41.4362240Z 2025-03-04T21:06:41.4362750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:06:41.4363342Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:06:41.4363836Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:06:41.4364327Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:06:41.4364619Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:06:41.4364849Z 2025-03-04T21:06:41.4365240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:41.4365723Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:06:41.4365966Z 2025-03-04T21:06:41.4366311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:41.4367229Z x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:06:41.4367943Z 2025-03-04T21:06:41.4368320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:06:41.4368823Z x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:06:41.4369123Z 2025-03-04T21:06:41.4369595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:06:41.4370663Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:06:41.4371411Z 2025-03-04T21:06:41.4371862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:06:41.4372900Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:06:41.4373731Z 2025-03-04T21:06:41.4374199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:06:41.4374805Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:06:41.4375213Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:06:41.4375506Z 2025-03-04T21:06:41.4376014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:06:41.4376633Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T21:06:41.4377020Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:06:41.4377420Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:06:41.4377715Z 2025-03-04T21:06:41.4378207Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:06:41.4378870Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:06:41.4379192Z 2025-03-04T21:06:41.4379711Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:06:41.4380345Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:06:41.4380704Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:06:41.4381045Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:06:41.4381306Z 2025-03-04T21:06:41.4381771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:41.4382367Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:06:41.4382663Z 2025-03-04T21:06:41.4383055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:41.4383548Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:06:41.4383807Z 2025-03-04T21:06:41.4384199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:41.4384689Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:41.4384994Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:41.4385314Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:06:41.4385573Z 2025-03-04T21:06:41.4385991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:41.4386484Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:41.4386781Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:41.4387147Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:41.4387414Z 2025-03-04T21:06:41.4387817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:41.4388497Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:41.4388793Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:06:41.4389055Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:06:41.4389302Z 2025-03-04T21:06:41.4389716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:41.4390217Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:41.4390509Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:06:41.4390782Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:06:41.4391020Z 2025-03-04T21:06:41.4391429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:41.4391932Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:41.4392257Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:06:41.4392494Z 2025-03-04T21:06:41.4392879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:41.4393372Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:41.4393693Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:06:41.4393925Z 2025-03-04T21:06:41.4394304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:41.4394802Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:41.4395122Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:06:41.4395354Z 2025-03-04T21:06:41.4395735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:41.4396261Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:41.4396605Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:06:41.4396836Z 2025-03-04T21:06:41.4397253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:41.4397774Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:41.4398032Z 2025-03-04T21:06:41.4398448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:41.4398963Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:41.4399212Z 2025-03-04T21:06:41.4399667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:41.4400225Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:41.4400541Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:06:41.4400869Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:41.4401231Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:06:41.4401515Z 2025-03-04T21:06:41.4401953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:41.4402493Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:41.4402818Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:06:41.4403163Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:41.4403509Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:06:41.4403770Z 2025-03-04T21:06:41.4404199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:41.4404715Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:41.4405050Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:41.4405399Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:06:41.4405658Z 2025-03-04T21:06:41.4406085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:41.4406594Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:41.4406934Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:41.4407293Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:06:41.4407553Z 2025-03-04T21:06:41.4407959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:41.4408435Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:41.4408707Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:41.4408948Z 2025-03-04T21:06:41.4409341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:41.4409813Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:41.4410079Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:41.4410314Z 2025-03-04T21:06:41.4410711Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:41.4411200Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:41.4411501Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:41.4411757Z 2025-03-04T21:06:41.4412153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:41.4412666Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:41.4412962Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:41.4413230Z 2025-03-04T21:06:41.4413746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:41.4414407Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:41.4414732Z 2025-03-04T21:06:41.4415173Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:41.4415727Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:06:41.4416014Z 2025-03-04T21:06:41.4416487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:06:41.4417098Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:06:41.4417392Z 2025-03-04T21:06:41.4417962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:06:41.4418639Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:06:41.4418894Z 2025-03-04T21:06:41.4419280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:41.4419776Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:06:41.4420038Z 2025-03-04T21:06:41.4420561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:06:41.4421167Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:06:41.4421440Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:06:41.4421716Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:06:41.4421952Z 2025-03-04T21:06:41.4422502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:06:41.4423183Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:06:41.4423649Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:06:41.4423999Z 2025-03-04T21:06:41.4424535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:06:41.4425198Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:06:41.4425481Z 2025-03-04T21:06:41.4425859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:41.4426384Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:06:41.4426665Z 2025-03-04T21:06:41.4427143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:06:41.4427745Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:06:41.4428006Z 2025-03-04T21:06:41.4428400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:41.4428902Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:06:41.4429161Z 2025-03-04T21:06:41.4429615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:06:41.4430174Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:06:41.4430430Z 2025-03-04T21:06:41.4430991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:06:41.4431669Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:06:41.4431989Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:41.4432324Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:06:41.4432670Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:06:41.4432920Z 2025-03-04T21:06:41.4433388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:06:41.4433930Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:06:41.4434169Z 2025-03-04T21:06:41.8401896Z 2025-03-04T21:06:41.8402605Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:41.8403347Z def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"): 2025-03-04T21:06:41.8403981Z l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_ 2025-03-04T21:06:41.8404291Z l_anchors_0_tensor = L_anchors_0_tensor 2025-03-04T21:06:41.8404600Z l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_ 2025-03-04T21:06:41.8404886Z 2025-03-04T21:06:41.8405545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:06:41.8406330Z pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4); l_pred_anchor_deltas_0_ = None 2025-03-04T21:06:41.8406720Z 2025-03-04T21:06:41.8407329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:06:41.8408047Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0); l_anchors_0_tensor = None 2025-03-04T21:06:41.8408446Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:06:41.8408794Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:06:41.8409060Z 2025-03-04T21:06:41.8409820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:41.8410435Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float(); pred_anchor_deltas_i = None 2025-03-04T21:06:41.8410799Z 2025-03-04T21:06:41.8411202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:41.8411776Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:06:41.8412047Z 2025-03-04T21:06:41.8412499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:41.8413011Z getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:41.8413329Z getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:41.8413776Z widths: "f32[328500][1]cpu" = getitem - getitem_1; getitem = getitem_1 = None 2025-03-04T21:06:41.8414048Z 2025-03-04T21:06:41.8414468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:41.8414983Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:41.8415294Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:41.8415630Z heights: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:06:41.8415908Z 2025-03-04T21:06:41.8416312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:41.8416808Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:41.8417079Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:06:41.8417351Z ctr_x: "f32[328500][1]cpu" = getitem_4 + mul; getitem_4 = mul = None 2025-03-04T21:06:41.8417603Z 2025-03-04T21:06:41.8418005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:41.8418527Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:41.8418824Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:06:41.8419108Z ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1; getitem_5 = mul_1 = None 2025-03-04T21:06:41.8419358Z 2025-03-04T21:06:41.8419785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:41.8420303Z getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:41.8420637Z dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0; getitem_6 = None 2025-03-04T21:06:41.8420883Z 2025-03-04T21:06:41.8421277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:41.8421793Z getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:41.8422124Z dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0; getitem_7 = None 2025-03-04T21:06:41.8422364Z 2025-03-04T21:06:41.8422755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:41.8423261Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:41.8423614Z dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:06:41.8423852Z 2025-03-04T21:06:41.8424244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:41.8424814Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:41.8425166Z dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:06:41.8425398Z 2025-03-04T21:06:41.8425860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:41.8426417Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:41.8426693Z 2025-03-04T21:06:41.8427105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:41.8427618Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:41.8427873Z 2025-03-04T21:06:41.8428299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:41.8428835Z getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:41.8429153Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10; dx = getitem_10 = None 2025-03-04T21:06:41.8429481Z getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:41.8429827Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11; mul_2 = getitem_11 = None 2025-03-04T21:06:41.8430087Z 2025-03-04T21:06:41.8430516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:41.8431049Z getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:41.8431365Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12; dy = getitem_12 = None 2025-03-04T21:06:41.8431681Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:41.8432023Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13; mul_3 = getitem_13 = None 2025-03-04T21:06:41.8432280Z 2025-03-04T21:06:41.8432691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:41.8433190Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:41.8433516Z getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:41.8433856Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14; exp = getitem_14 = None 2025-03-04T21:06:41.8434104Z 2025-03-04T21:06:41.8434517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:41.8435026Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:41.8435371Z getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:41.8435728Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15; exp_1 = getitem_15 = None 2025-03-04T21:06:41.8435984Z 2025-03-04T21:06:41.8436411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:41.8436878Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:41.8437171Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:41.8437416Z 2025-03-04T21:06:41.8437820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:41.8438291Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:41.8438581Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:41.8438863Z 2025-03-04T21:06:41.8439283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:41.8439762Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:41.8440064Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:41.8440321Z 2025-03-04T21:06:41.8440718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:41.8441193Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:41.8441487Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:41.8441750Z 2025-03-04T21:06:41.8442177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:41.8442753Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:41.8443045Z 2025-03-04T21:06:41.8443459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:41.8443994Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:06:41.8444276Z 2025-03-04T21:06:41.8444739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:06:41.8445338Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:06:41.8445625Z 2025-03-04T21:06:41.8446181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:06:41.8446944Z arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:06:41.8447193Z 2025-03-04T21:06:41.8447571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:41.8448053Z batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:06:41.8448310Z 2025-03-04T21:06:41.8448823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:06:41.8449473Z topk = l_pred_objectness_logits_0_.topk(6000, dim = 1); l_pred_objectness_logits_0_ = None 2025-03-04T21:06:41.8449808Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:06:41.8450117Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:06:41.8450350Z 2025-03-04T21:06:41.8450889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:06:41.8451574Z getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:06:41.8452039Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)]; proposals_i_1 = getitem_18 = topk_idx = None 2025-03-04T21:06:41.8452404Z 2025-03-04T21:06:41.8452936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:06:41.8453719Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:06:41.8454021Z 2025-03-04T21:06:41.8454429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:41.8454956Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:06:41.8455238Z 2025-03-04T21:06:41.8455740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:06:41.8456357Z getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:06:41.8456638Z 2025-03-04T21:06:41.8457050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:41.8457578Z tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32); getitem_20 = None 2025-03-04T21:06:41.8457854Z 2025-03-04T21:06:41.8458345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:06:41.8458940Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:06:41.8459216Z 2025-03-04T21:06:41.8459805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:06:41.8460517Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor); tensor = None 2025-03-04T21:06:41.8460844Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:41.8461189Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:06:41.8461550Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:06:41.8461823Z 2025-03-04T21:06:41.8462304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:06:41.8462879Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:06:41.8463136Z 2025-03-04T21:06:57.1155608Z 2025-03-04T21:06:57.1156499Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:57.1158438Z def forward(self, L_stack0_: "f32[3231, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"): 2025-03-04T21:06:57.1159968Z l_stack0_ = L_stack0_ 2025-03-04T21:06:57.1160463Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:06:57.1161123Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:06:57.1161707Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:06:57.1162291Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:06:57.1162796Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:06:57.1163219Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:06:57.1163637Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:06:57.1164054Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:06:57.1164396Z 2025-03-04T21:06:57.1164990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T21:06:57.1165664Z mean: "f32[3231, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T21:06:57.1165951Z 2025-03-04T21:06:57.1166375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:06:57.1167435Z scores: "f32[3231, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:06:57.1168179Z 2025-03-04T21:06:57.1168617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:06:57.1169670Z proposal_deltas: "f32[3231, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:06:57.1170532Z 2025-03-04T21:06:57.1170934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:57.1171431Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:57.1171700Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:06:57.1171948Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:06:57.1172239Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:57.1172518Z getitem_2: "Sym(1231 - s0)" = size_1[0] 2025-03-04T21:06:57.1172775Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:06:57.1173007Z 2025-03-04T21:06:57.1173425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:06:57.1174422Z proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:06:57.1175293Z 2025-03-04T21:06:57.1176010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:57.1176694Z deltas: "f32[3231, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:06:57.1176988Z 2025-03-04T21:06:57.1177417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:57.1178016Z boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:06:57.1178334Z 2025-03-04T21:06:57.1178788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:57.1179352Z getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:57.1179699Z getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:57.1180057Z widths: "f32[3231][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:57.1180351Z 2025-03-04T21:06:57.1180808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:57.1181362Z getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:57.1181691Z getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:57.1182044Z heights: "f32[3231][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:06:57.1182338Z 2025-03-04T21:06:57.1182789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:57.1183334Z getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:57.1183620Z mul: "f32[3231][1]cpu" = 0.5 * widths 2025-03-04T21:06:57.1183913Z ctr_x: "f32[3231][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:06:57.1184184Z 2025-03-04T21:06:57.1184628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:57.1185147Z getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:57.1185451Z mul_1: "f32[3231][1]cpu" = 0.5 * heights 2025-03-04T21:06:57.1185729Z ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:06:57.1185979Z 2025-03-04T21:06:57.1186411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:57.1186937Z getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:57.1187273Z dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:06:57.1187517Z 2025-03-04T21:06:57.1187986Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:57.1188770Z getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:57.1189161Z dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:06:57.1189402Z 2025-03-04T21:06:57.1189802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:57.1190351Z getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:57.1190714Z dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:06:57.1190954Z 2025-03-04T21:06:57.1191362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:57.1191921Z getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:57.1192388Z dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:06:57.1192637Z 2025-03-04T21:06:57.1193085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:57.1193636Z dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:57.1193909Z 2025-03-04T21:06:57.1194344Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:57.1194886Z dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:57.1195159Z 2025-03-04T21:06:57.1195617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:57.1196189Z getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:57.1196521Z mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:06:57.1196869Z getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:57.1197330Z pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:06:57.1197712Z 2025-03-04T21:06:57.1198404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:57.1199081Z getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:57.1199416Z mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:06:57.1199758Z getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:57.1200114Z pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:06:57.1200381Z 2025-03-04T21:06:57.1200950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:57.1201598Z exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:57.1201927Z getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:57.1202275Z pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:06:57.1202533Z 2025-03-04T21:06:57.1203010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:57.1203541Z exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:57.1203884Z getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:57.1204225Z pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:06:57.1204482Z 2025-03-04T21:06:57.1204915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:57.1205405Z mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:57.1205672Z x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:57.1205910Z 2025-03-04T21:06:57.1206314Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:57.1206771Z mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:57.1207036Z y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:57.1207273Z 2025-03-04T21:06:57.1207675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:57.1208153Z mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:57.1208449Z x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:57.1208698Z 2025-03-04T21:06:57.1209089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:57.1209571Z mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:57.1209859Z y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:57.1210107Z 2025-03-04T21:06:57.1210549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:57.1211140Z pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:57.1211433Z 2025-03-04T21:06:57.1211859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:57.1212420Z predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320)); pred_boxes = None 2025-03-04T21:06:57.1212710Z 2025-03-04T21:06:57.1213166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:06:57.1213791Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:06:57.1214161Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:06:57.1214452Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:06:57.1214761Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:06:57.1215161Z getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:06:57.1215431Z 2025-03-04T21:06:57.1215851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:57.1216500Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:06:57.1216879Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:06:57.1217165Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:06:57.1217561Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:06:57.1217940Z getitem_26: "Sym(1231 - s0)" = size_3[0] 2025-03-04T21:06:57.1218223Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:06:57.1218473Z 2025-03-04T21:06:57.1218937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:06:57.1219534Z probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:06:57.1219847Z 2025-03-04T21:06:57.1220323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:06:57.1220971Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:06:57.1221359Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:06:57.1221675Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:06:57.1221999Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:06:57.1222337Z getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:06:57.1222611Z 2025-03-04T21:06:57.1223202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:06:57.1223954Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:06:57.1224325Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:57.1224690Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:06:57.1225056Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:06:57.1225376Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:06:57.1225632Z 2025-03-04T21:06:57.1226091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:06:57.1226617Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:06:57.1226861Z 2025-03-04T21:06:57.1227004Z 2025-03-04T21:06:57.1227106Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:57.1228515Z def forward(self, L_stack0_: "f32[3231, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"): 2025-03-04T21:06:57.1229895Z l_stack0_ = L_stack0_ 2025-03-04T21:06:57.1230306Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:06:57.1230874Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:06:57.1231457Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:06:57.1232032Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:06:57.1232538Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:06:57.1232945Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:06:57.1233346Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:06:57.1233743Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:06:57.1234038Z 2025-03-04T21:06:57.1234574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T21:06:57.1235221Z mean: "f32[3231, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T21:06:57.1235493Z 2025-03-04T21:06:57.1235893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:06:57.1236871Z scores: "f32[3231, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:06:57.1237595Z 2025-03-04T21:06:57.1238010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:06:57.1239031Z proposal_deltas: "f32[3231, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:06:57.1239790Z 2025-03-04T21:06:57.1240171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:57.1240636Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:57.1240898Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:06:57.1241135Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:06:57.1241415Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:57.1241713Z getitem_2: "Sym(1231 - s0)" = size_1[0] 2025-03-04T21:06:57.1241962Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:06:57.1242186Z 2025-03-04T21:06:57.1242559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:06:57.1243517Z proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:06:57.1244248Z 2025-03-04T21:06:57.1244724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:57.1245309Z deltas: "f32[3231, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:06:57.1245576Z 2025-03-04T21:06:57.1245960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:57.1246491Z boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:06:57.1246782Z 2025-03-04T21:06:57.1247172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:57.1247744Z getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:57.1248175Z getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:57.1248624Z widths: "f32[3231][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:57.1249021Z 2025-03-04T21:06:57.1249494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:57.1250232Z getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:57.1250549Z getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:57.1250859Z heights: "f32[3231][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:06:57.1251124Z 2025-03-04T21:06:57.1251530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:57.1252033Z getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:57.1252297Z mul: "f32[3231][1]cpu" = 0.5 * widths 2025-03-04T21:06:57.1252560Z ctr_x: "f32[3231][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:06:57.1252804Z 2025-03-04T21:06:57.1253229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:57.1253763Z getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:57.1254076Z mul_1: "f32[3231][1]cpu" = 0.5 * heights 2025-03-04T21:06:57.1254348Z ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:06:57.1254601Z 2025-03-04T21:06:57.1255135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:57.1255720Z getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:57.1256096Z dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:06:57.1256349Z 2025-03-04T21:06:57.1256774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:57.1257336Z getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:57.1257674Z dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:06:57.1257920Z 2025-03-04T21:06:57.1258328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:57.1258881Z getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:57.1259206Z dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:06:57.1259459Z 2025-03-04T21:06:57.1259856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:57.1260401Z getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:57.1260768Z dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:06:57.1261024Z 2025-03-04T21:06:57.1261457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:57.1261991Z dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:57.1262254Z 2025-03-04T21:06:57.1262676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:57.1263208Z dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:57.1263467Z 2025-03-04T21:06:57.1263901Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:57.1264445Z getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:57.1264766Z mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:06:57.1265099Z getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:57.1265448Z pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:06:57.1265707Z 2025-03-04T21:06:57.1266144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:57.1266686Z getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:57.1267003Z mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:06:57.1267331Z getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:57.1267678Z pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:06:57.1267936Z 2025-03-04T21:06:57.1268364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:57.1268872Z exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:57.1269198Z getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:57.1269544Z pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:06:57.1269797Z 2025-03-04T21:06:57.1270224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:57.1270750Z exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:57.1271094Z getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:57.1271455Z pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:06:57.1271721Z 2025-03-04T21:06:57.1272164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:57.1272731Z mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:57.1273004Z x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:57.1273247Z 2025-03-04T21:06:57.1273660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:57.1274162Z mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:57.1274451Z y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:57.1274695Z 2025-03-04T21:06:57.1275100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:57.1275595Z mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:57.1275894Z x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:57.1276161Z 2025-03-04T21:06:57.1276554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:57.1277027Z mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:57.1277320Z y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:57.1277575Z 2025-03-04T21:06:57.1278016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:57.1278612Z pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:57.1278915Z 2025-03-04T21:06:57.1279352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:57.1279931Z predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320)); pred_boxes = None 2025-03-04T21:06:57.1280230Z 2025-03-04T21:06:57.1280693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:06:57.1281329Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:06:57.1281700Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:06:57.1281999Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:06:57.1282312Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:06:57.1282641Z getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:06:57.1282913Z 2025-03-04T21:06:57.1283301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:57.1283872Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:06:57.1284232Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:06:57.1284487Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:06:57.1284859Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:06:57.1285224Z getitem_26: "Sym(1231 - s0)" = size_3[0] 2025-03-04T21:06:57.1285496Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:06:57.1285724Z 2025-03-04T21:06:57.1286163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:06:57.1286754Z probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:06:57.1287057Z 2025-03-04T21:06:57.1287551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:06:57.1288395Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:06:57.1288780Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:06:57.1289083Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:06:57.1289400Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:06:57.1289732Z getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:06:57.1290004Z 2025-03-04T21:06:57.1290572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:06:57.1291284Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:06:57.1291638Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:57.1291989Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:06:57.1292339Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:06:57.1292644Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:06:57.1292889Z 2025-03-04T21:06:57.1293344Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:06:57.1293883Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:06:57.1294125Z 2025-03-04T21:06:57.1294269Z 2025-03-04T21:06:57.1294370Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:57.1295944Z def forward(self, L_stack0_: "f32[3231, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"): 2025-03-04T21:06:57.1297474Z l_stack0_ = L_stack0_ 2025-03-04T21:06:57.1297923Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:06:57.1298600Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:06:57.1299290Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:06:57.1300347Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:06:57.1300919Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:06:57.1301386Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:06:57.1301881Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:06:57.1302334Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:06:57.1302692Z 2025-03-04T21:06:57.1303421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T21:06:57.1304195Z mean: "f32[3231, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T21:06:57.1304502Z 2025-03-04T21:06:57.1304983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:06:57.1306701Z scores: "f32[3231, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:06:57.1307539Z 2025-03-04T21:06:57.1308021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:06:57.1309398Z proposal_deltas: "f32[3231, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:06:57.1310253Z 2025-03-04T21:06:57.1310677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:57.1311200Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:57.1311487Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:06:57.1311751Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:06:57.1312063Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:57.1312359Z getitem_2: "Sym(1231 - s0)" = size_1[0] 2025-03-04T21:06:57.1312639Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:06:57.1312886Z 2025-03-04T21:06:57.1313308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:06:57.1314364Z proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:06:57.1315173Z 2025-03-04T21:06:57.1315690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:57.1316330Z deltas: "f32[3231, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:06:57.1316660Z 2025-03-04T21:06:57.1317106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:57.1317722Z boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:06:57.1318034Z 2025-03-04T21:06:57.1318484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:57.1318999Z getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:57.1319306Z getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:57.1319641Z widths: "f32[3231][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:57.1319938Z 2025-03-04T21:06:57.1320371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:57.1320902Z getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:57.1321199Z getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:57.1321518Z heights: "f32[3231][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:06:57.1321792Z 2025-03-04T21:06:57.1322214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:57.1322733Z getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:57.1322993Z mul: "f32[3231][1]cpu" = 0.5 * widths 2025-03-04T21:06:57.1323258Z ctr_x: "f32[3231][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:06:57.1323496Z 2025-03-04T21:06:57.1323894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:57.1324400Z getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:57.1324688Z mul_1: "f32[3231][1]cpu" = 0.5 * heights 2025-03-04T21:06:57.1324955Z ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:06:57.1325199Z 2025-03-04T21:06:57.1325610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:57.1326127Z getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:57.1326458Z dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:06:57.1326695Z 2025-03-04T21:06:57.1327086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:57.1327594Z getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:57.1327918Z dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:06:57.1328153Z 2025-03-04T21:06:57.1328543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:57.1329049Z getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:57.1329374Z dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:06:57.1329610Z 2025-03-04T21:06:57.1330006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:57.1330541Z getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:57.1330909Z dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:06:57.1331149Z 2025-03-04T21:06:57.1331584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:57.1332139Z dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:57.1332400Z 2025-03-04T21:06:57.1332839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:57.1333377Z dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:57.1333632Z 2025-03-04T21:06:57.1334067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:57.1334633Z getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:57.1335061Z mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:06:57.1335459Z getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:57.1335866Z pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:06:57.1336166Z 2025-03-04T21:06:57.1336677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:57.1337317Z getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:57.1337682Z mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:06:57.1338073Z getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:57.1338473Z pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:06:57.1338777Z 2025-03-04T21:06:57.1339275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:57.1339871Z exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:57.1340251Z getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:57.1340650Z pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:06:57.1340933Z 2025-03-04T21:06:57.1341430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:57.1342022Z exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:57.1342409Z getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:57.1342817Z pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:06:57.1343112Z 2025-03-04T21:06:57.1343584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:57.1344132Z mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:57.1344443Z x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:57.1344718Z 2025-03-04T21:06:57.1345189Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:57.1345753Z mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:57.1346061Z y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:57.1346372Z 2025-03-04T21:06:57.1346820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:57.1347358Z mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:57.1347676Z x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:57.1347954Z 2025-03-04T21:06:57.1348363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:57.1348852Z mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:57.1349151Z y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:57.1349408Z 2025-03-04T21:06:57.1349855Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:57.1350460Z pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:57.1350765Z 2025-03-04T21:06:57.1351200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:57.1351777Z predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320)); pred_boxes = None 2025-03-04T21:06:57.1352071Z 2025-03-04T21:06:57.1352531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:06:57.1353160Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:06:57.1353539Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:06:57.1353841Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:06:57.1354153Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:06:57.1354484Z getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:06:57.1354757Z 2025-03-04T21:06:57.1355153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:57.1355733Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:06:57.1356094Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:06:57.1356348Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:06:57.1356726Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:06:57.1357085Z getitem_26: "Sym(1231 - s0)" = size_3[0] 2025-03-04T21:06:57.1357342Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:06:57.1357570Z 2025-03-04T21:06:57.1358015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:06:57.1358595Z probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:06:57.1358893Z 2025-03-04T21:06:57.1359372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:06:57.1359993Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:06:57.1360385Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:06:57.1360686Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:06:57.1360998Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:06:57.1361339Z getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:06:57.1361632Z 2025-03-04T21:06:57.1362207Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:06:57.1362920Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:06:57.1363288Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:57.1363633Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:06:57.1363984Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:06:57.1364289Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:06:57.1364535Z 2025-03-04T21:06:57.1364983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:06:57.1365515Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:06:57.1365758Z 2025-03-04T21:06:57.1365904Z 2025-03-04T21:06:57.1366005Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:57.1367391Z def forward(self, L_stack0_: "f32[3231, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"): 2025-03-04T21:06:57.1368715Z l_stack0_ = L_stack0_ 2025-03-04T21:06:57.1369110Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:06:57.1369678Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:06:57.1370246Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:06:57.1370811Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:06:57.1371291Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:06:57.1371703Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:06:57.1372107Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:06:57.1372505Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:06:57.1372803Z 2025-03-04T21:06:57.1373358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T21:06:57.1374011Z mean: "f32[3231, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T21:06:57.1374280Z 2025-03-04T21:06:57.1374679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:06:57.1375782Z scores: "f32[3231, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:06:57.1376735Z 2025-03-04T21:06:57.1377211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:06:57.1378285Z proposal_deltas: "f32[3231, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:06:57.1379065Z 2025-03-04T21:06:57.1379461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:57.1379949Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:57.1380214Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:06:57.1380458Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:06:57.1380744Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:57.1381016Z getitem_2: "Sym(1231 - s0)" = size_1[0] 2025-03-04T21:06:57.1381270Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:06:57.1381497Z 2025-03-04T21:06:57.1381881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:06:57.1382858Z proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:06:57.1383598Z 2025-03-04T21:06:57.1384083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:57.1384682Z deltas: "f32[3231, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:06:57.1384964Z 2025-03-04T21:06:57.1385377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:57.1385919Z boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:06:57.1386208Z 2025-03-04T21:06:57.1386627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:57.1387149Z getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:57.1387469Z getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:57.1387827Z widths: "f32[3231][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:57.1388221Z 2025-03-04T21:06:57.1388657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:57.1389231Z getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:57.1389531Z getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:57.1389905Z heights: "f32[3231][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:06:57.1390208Z 2025-03-04T21:06:57.1390626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:57.1391129Z getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:57.1391397Z mul: "f32[3231][1]cpu" = 0.5 * widths 2025-03-04T21:06:57.1391666Z ctr_x: "f32[3231][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:06:57.1391913Z 2025-03-04T21:06:57.1392330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:57.1392852Z getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:57.1393149Z mul_1: "f32[3231][1]cpu" = 0.5 * heights 2025-03-04T21:06:57.1393420Z ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:06:57.1393672Z 2025-03-04T21:06:57.1394085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:57.1394620Z getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:57.1394950Z dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:06:57.1395196Z 2025-03-04T21:06:57.1395595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:57.1396115Z getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:57.1396436Z dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:06:57.1396669Z 2025-03-04T21:06:57.1397054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:57.1397556Z getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:57.1397876Z dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:06:57.1398111Z 2025-03-04T21:06:57.1398502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:57.1399044Z getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:57.1399388Z dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:06:57.1399621Z 2025-03-04T21:06:57.1400053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:57.1400584Z dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:57.1400845Z 2025-03-04T21:06:57.1401311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:57.1401835Z dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:57.1402113Z 2025-03-04T21:06:57.1402548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:57.1403088Z getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:57.1403425Z mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:06:57.1403783Z getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:57.1404136Z pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:06:57.1404403Z 2025-03-04T21:06:57.1404846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:57.1405383Z getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:57.1405699Z mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:06:57.1406029Z getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:57.1406375Z pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:06:57.1406633Z 2025-03-04T21:06:57.1407049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:57.1407560Z exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:57.1407877Z getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:57.1408220Z pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:06:57.1408476Z 2025-03-04T21:06:57.1408895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:57.1409408Z exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:57.1409734Z getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:57.1410080Z pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:06:57.1410329Z 2025-03-04T21:06:57.1410723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:57.1411177Z mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:57.1411435Z x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:57.1411672Z 2025-03-04T21:06:57.1412058Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:57.1412508Z mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:57.1412763Z y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:57.1412991Z 2025-03-04T21:06:57.1413371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:57.1413843Z mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:57.1414160Z x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:57.1414408Z 2025-03-04T21:06:57.1414812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:57.1427186Z mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:57.1427531Z y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:57.1427800Z 2025-03-04T21:06:57.1428398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:57.1429052Z pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:57.1429360Z 2025-03-04T21:06:57.1429803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:57.1430388Z predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320)); pred_boxes = None 2025-03-04T21:06:57.1430694Z 2025-03-04T21:06:57.1431156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:06:57.1431789Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:06:57.1432165Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:06:57.1432467Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:06:57.1432783Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:06:57.1433109Z getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:06:57.1433381Z 2025-03-04T21:06:57.1433776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:57.1434350Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:06:57.1434704Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:06:57.1434954Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:06:57.1435326Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:06:57.1435687Z getitem_26: "Sym(1231 - s0)" = size_3[0] 2025-03-04T21:06:57.1435942Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:06:57.1436166Z 2025-03-04T21:06:57.1436599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:06:57.1437169Z probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:06:57.1437467Z 2025-03-04T21:06:57.1437909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:06:57.1438526Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:06:57.1438893Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:06:57.1439181Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:06:57.1439484Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:06:57.1439844Z getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:06:57.1440114Z 2025-03-04T21:06:57.1440674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:06:57.1441398Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:06:57.1441748Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:57.1442117Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:06:57.1442480Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:06:57.1442782Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:06:57.1443025Z 2025-03-04T21:06:57.1443470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:06:57.1444004Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:06:57.1444243Z 2025-03-04T21:06:58.7570143Z 2025-03-04T21:06:58.7570880Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:58.7571989Z def forward(self, L_predictions_0_: "f32[3231, 81][81, 1]cpu", L_predictions_1_: "f32[3231, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"): 2025-03-04T21:06:58.7572997Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:06:58.7573269Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:06:58.7573638Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:06:58.7574081Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:06:58.7574520Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:06:58.7575081Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:06:58.7575444Z 2025-03-04T21:06:58.7575938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:58.7576489Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:58.7576770Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:06:58.7577024Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:06:58.7577383Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:58.7577701Z getitem_2: "Sym(1231 - s0)" = size_1[0] 2025-03-04T21:06:58.7577978Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:06:58.7578246Z 2025-03-04T21:06:58.7578732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:06:58.7579830Z proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:06:58.7580648Z 2025-03-04T21:06:58.7581257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:58.7582259Z deltas: "f32[3231, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:06:58.7582562Z 2025-03-04T21:06:58.7583017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:58.7583689Z boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:06:58.7583979Z 2025-03-04T21:06:58.7584434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:58.7584993Z getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:58.7585300Z getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:58.7585622Z widths: "f32[3231][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:58.7585892Z 2025-03-04T21:06:58.7586308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:58.7586813Z getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:58.7587110Z getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:58.7587429Z heights: "f32[3231][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:06:58.7587698Z 2025-03-04T21:06:58.7588308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:58.7588820Z getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:58.7589091Z mul: "f32[3231][1]cpu" = 0.5 * widths 2025-03-04T21:06:58.7589358Z ctr_x: "f32[3231][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:06:58.7589602Z 2025-03-04T21:06:58.7590007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:58.7590585Z getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:58.7590878Z mul_1: "f32[3231][1]cpu" = 0.5 * heights 2025-03-04T21:06:58.7591147Z ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:06:58.7591398Z 2025-03-04T21:06:58.7591832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:58.7592355Z getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:58.7592694Z dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:06:58.7592935Z 2025-03-04T21:06:58.7593333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:58.7593851Z getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:58.7594177Z dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:06:58.7594414Z 2025-03-04T21:06:58.7594808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:58.7595322Z getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:58.7595645Z dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:06:58.7595882Z 2025-03-04T21:06:58.7596317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:58.7596890Z getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:58.7597240Z dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:06:58.7597474Z 2025-03-04T21:06:58.7597931Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:58.7598493Z dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:58.7598756Z 2025-03-04T21:06:58.7599183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:58.7599709Z dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:58.7599969Z 2025-03-04T21:06:58.7600405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:58.7600949Z getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:58.7601275Z mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:06:58.7601607Z getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:58.7601955Z pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:06:58.7602214Z 2025-03-04T21:06:58.7602653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:58.7603189Z getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:58.7603511Z mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:06:58.7603840Z getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:58.7604184Z pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:06:58.7604447Z 2025-03-04T21:06:58.7604891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:58.7605393Z exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:58.7605727Z getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:58.7606068Z pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:06:58.7606319Z 2025-03-04T21:06:58.7606739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:58.7607245Z exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:58.7607584Z getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:58.7607940Z pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:06:58.7608196Z 2025-03-04T21:06:58.7608601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:58.7609093Z mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:58.7609366Z x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:58.7609605Z 2025-03-04T21:06:58.7610028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:58.7610490Z mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:58.7610808Z y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:58.7611141Z 2025-03-04T21:06:58.7612055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:58.7612590Z mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:58.7612897Z x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:58.7613154Z 2025-03-04T21:06:58.7613553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:58.7614028Z mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:58.7614323Z y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:58.7614572Z 2025-03-04T21:06:58.7615152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:58.7615824Z pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:58.7616163Z 2025-03-04T21:06:58.7616604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:58.7617186Z predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320)); pred_boxes = None 2025-03-04T21:06:58.7617496Z 2025-03-04T21:06:58.7617951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:06:58.7618570Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:06:58.7618937Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:06:58.7619233Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:06:58.7619542Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:06:58.7619864Z getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:06:58.7620130Z 2025-03-04T21:06:58.7620517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:58.7621086Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:06:58.7621439Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:06:58.7621688Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:06:58.7622059Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:06:58.7622411Z getitem_26: "Sym(1231 - s0)" = size_3[0] 2025-03-04T21:06:58.7622664Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:06:58.7622889Z 2025-03-04T21:06:58.7623338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:06:58.7623947Z probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:06:58.7624296Z 2025-03-04T21:06:58.7624754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:06:58.7625368Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:06:58.7625764Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:06:58.7626079Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:06:58.7626393Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:06:58.7626722Z getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:06:58.7626995Z 2025-03-04T21:06:58.7627565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:06:58.7628286Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:06:58.7628640Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:58.7628991Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:06:58.7629348Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:06:58.7629651Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:06:58.7629898Z 2025-03-04T21:06:58.7630359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:06:58.7633654Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:06:58.7634078Z 2025-03-04T21:06:58.7634231Z 2025-03-04T21:06:58.7634338Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:58.7635202Z def forward(self, L_predictions_0_: "f32[3231, 81][81, 1]cpu", L_predictions_1_: "f32[3231, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"): 2025-03-04T21:06:58.7636032Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:06:58.7636271Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:06:58.7636604Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:06:58.7637025Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:06:58.7637438Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:06:58.7637843Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:06:58.7638152Z 2025-03-04T21:06:58.7638562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:58.7639046Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:58.7639311Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:06:58.7639555Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:06:58.7639840Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:58.7640178Z getitem_2: "Sym(1231 - s0)" = size_1[0] 2025-03-04T21:06:58.7640439Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:06:58.7640672Z 2025-03-04T21:06:58.7641065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:06:58.7642135Z proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:06:58.7642893Z 2025-03-04T21:06:58.7643367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:58.7643966Z deltas: "f32[3231, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:06:58.7644247Z 2025-03-04T21:06:58.7644657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:58.7645201Z boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:06:58.7645490Z 2025-03-04T21:06:58.7645909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:58.7646426Z getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:58.7646739Z getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:58.7647064Z widths: "f32[3231][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:58.7647336Z 2025-03-04T21:06:58.7647759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:58.7648267Z getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:58.7648572Z getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:58.7648895Z heights: "f32[3231][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:06:58.7649164Z 2025-03-04T21:06:58.7649578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:58.7650079Z getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:58.7650342Z mul: "f32[3231][1]cpu" = 0.5 * widths 2025-03-04T21:06:58.7650604Z ctr_x: "f32[3231][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:06:58.7650851Z 2025-03-04T21:06:58.7651261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:58.7651782Z getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:58.7652078Z mul_1: "f32[3231][1]cpu" = 0.5 * heights 2025-03-04T21:06:58.7652350Z ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:06:58.7652605Z 2025-03-04T21:06:58.7653045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:58.7653575Z getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:58.7653925Z dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:06:58.7654195Z 2025-03-04T21:06:58.7654603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:58.7655387Z getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:58.7655750Z dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:06:58.7656015Z 2025-03-04T21:06:58.7656492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:58.7657054Z getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:58.7657400Z dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:06:58.7657664Z 2025-03-04T21:06:58.7658104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:58.7658705Z getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:58.7659096Z dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:06:58.7659363Z 2025-03-04T21:06:58.7659849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:58.7660451Z dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:58.7660744Z 2025-03-04T21:06:58.7661211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:58.7661804Z dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:58.7662091Z 2025-03-04T21:06:58.7662576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:58.7663183Z getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:58.7663543Z mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:06:58.7663918Z getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:58.7664304Z pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:06:58.7664569Z 2025-03-04T21:06:58.7665016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:58.7665568Z getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:58.7665892Z mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:06:58.7666230Z getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:58.7666608Z pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:06:58.7666873Z 2025-03-04T21:06:58.7667314Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:58.7667852Z exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:58.7668201Z getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:58.7668573Z pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:06:58.7668835Z 2025-03-04T21:06:58.7669267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:58.7669805Z exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:58.7670149Z getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:58.7670528Z pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:06:58.7670813Z 2025-03-04T21:06:58.7671223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:58.7671695Z mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:58.7671971Z x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:58.7672215Z 2025-03-04T21:06:58.7672623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:58.7673094Z mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:58.7673365Z y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:58.7673605Z 2025-03-04T21:06:58.7674004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:58.7674507Z mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:58.7674825Z x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:58.7675082Z 2025-03-04T21:06:58.7675489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:58.7676074Z mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:58.7676544Z y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:58.7676824Z 2025-03-04T21:06:58.7677286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:58.7677910Z pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:58.7678211Z 2025-03-04T21:06:58.7678645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:58.7679222Z predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320)); pred_boxes = None 2025-03-04T21:06:58.7679520Z 2025-03-04T21:06:58.7679981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:06:58.7680614Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:06:58.7680991Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:06:58.7681289Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:06:58.7681602Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:06:58.7681928Z getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:06:58.7682198Z 2025-03-04T21:06:58.7682611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:58.7683189Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:06:58.7683599Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:06:58.7683852Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:06:58.7684222Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:06:58.7684605Z getitem_26: "Sym(1231 - s0)" = size_3[0] 2025-03-04T21:06:58.7684884Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:06:58.7685111Z 2025-03-04T21:06:58.7685544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:06:58.7686162Z probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:06:58.7686502Z 2025-03-04T21:06:58.7686958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:06:58.7687579Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:06:58.7687955Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:06:58.7688414Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:06:58.7688736Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:06:58.7689074Z getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:06:58.7689349Z 2025-03-04T21:06:58.7689942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:06:58.7690684Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:06:58.7691048Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:58.7691409Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:06:58.7691766Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:06:58.7692073Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:06:58.7692316Z 2025-03-04T21:06:58.7692779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:06:58.7693329Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:06:58.7693578Z 2025-03-04T21:06:58.7693724Z 2025-03-04T21:06:58.7693825Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:58.7694677Z def forward(self, L_predictions_0_: "f32[3231, 81][81, 1]cpu", L_predictions_1_: "f32[3231, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"): 2025-03-04T21:06:58.7695619Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:06:58.7695879Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:06:58.7696240Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:06:58.7696743Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:06:58.7697193Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:06:58.7697670Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:06:58.7697997Z 2025-03-04T21:06:58.7698431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:58.7698979Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:58.7699293Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:06:58.7699554Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:06:58.7699860Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:58.7700151Z getitem_2: "Sym(1231 - s0)" = size_1[0] 2025-03-04T21:06:58.7700426Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:06:58.7700669Z 2025-03-04T21:06:58.7701084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:06:58.7702158Z proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:06:58.7702962Z 2025-03-04T21:06:58.7703482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:58.7704071Z deltas: "f32[3231, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:06:58.7704352Z 2025-03-04T21:06:58.7704760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:58.7705300Z boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:06:58.7705586Z 2025-03-04T21:06:58.7705991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:58.7706513Z getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:58.7706820Z getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:58.7707146Z widths: "f32[3231][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:58.7707416Z 2025-03-04T21:06:58.7707842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:58.7708346Z getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:58.7708653Z getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:58.7708977Z heights: "f32[3231][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:06:58.7709249Z 2025-03-04T21:06:58.7709665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:58.7710152Z getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:58.7710408Z mul: "f32[3231][1]cpu" = 0.5 * widths 2025-03-04T21:06:58.7710665Z ctr_x: "f32[3231][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:06:58.7710901Z 2025-03-04T21:06:58.7711322Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:58.7711848Z getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:58.7712136Z mul_1: "f32[3231][1]cpu" = 0.5 * heights 2025-03-04T21:06:58.7712408Z ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:06:58.7712657Z 2025-03-04T21:06:58.7713083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:58.7713621Z getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:58.7713952Z dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:06:58.7714189Z 2025-03-04T21:06:58.7714582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:58.7715095Z getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:58.7715424Z dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:06:58.7715662Z 2025-03-04T21:06:58.7716058Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:58.7716566Z getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:58.7716891Z dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:06:58.7717123Z 2025-03-04T21:06:58.7717511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:58.7718048Z getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:58.7718397Z dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:06:58.7718628Z 2025-03-04T21:06:58.7719052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:58.7719581Z dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:58.7719843Z 2025-03-04T21:06:58.7720260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:58.7720789Z dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:58.7721045Z 2025-03-04T21:06:58.7721476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:58.7722020Z getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:58.7722338Z mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:06:58.7722674Z getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:58.7723024Z pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:06:58.7723286Z 2025-03-04T21:06:58.7723714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:58.7724272Z getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:58.7724588Z mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:06:58.7724935Z getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:58.7725280Z pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:06:58.7725540Z 2025-03-04T21:06:58.7725985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:58.7726615Z exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:58.7726947Z getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:58.7727294Z pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:06:58.7727550Z 2025-03-04T21:06:58.7727972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:58.7728484Z exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:58.7728817Z getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:58.7729168Z pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:06:58.7729423Z 2025-03-04T21:06:58.7729828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:58.7730294Z mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:58.7730561Z x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:58.7730803Z 2025-03-04T21:06:58.7731207Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:58.7731679Z mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:58.7731945Z y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:58.7732183Z 2025-03-04T21:06:58.7732587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:58.7733073Z mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:58.7733373Z x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:58.7733627Z 2025-03-04T21:06:58.7734028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:58.7734508Z mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:58.7734805Z y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:58.7735184Z 2025-03-04T21:06:58.7735678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:58.7736326Z pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:58.7736634Z 2025-03-04T21:06:58.7737081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:58.7737709Z predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320)); pred_boxes = None 2025-03-04T21:06:58.7738009Z 2025-03-04T21:06:58.7738468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:06:58.7739120Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:06:58.7739494Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:06:58.7739811Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:06:58.7740151Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:06:58.7740472Z getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:06:58.7740743Z 2025-03-04T21:06:58.7741138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:58.7741715Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:06:58.7742081Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:06:58.7742334Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:06:58.7742711Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:06:58.7743077Z getitem_26: "Sym(1231 - s0)" = size_3[0] 2025-03-04T21:06:58.7743350Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:06:58.7743577Z 2025-03-04T21:06:58.7744013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:06:58.7744630Z probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:06:58.7744969Z 2025-03-04T21:06:58.7745423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:06:58.7746043Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:06:58.7746434Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:06:58.7746737Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:06:58.7747053Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:06:58.7747369Z getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:06:58.7747630Z 2025-03-04T21:06:58.7748181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:06:58.7748876Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:06:58.7749215Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:58.7749554Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:06:58.7749894Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:06:58.7750190Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:06:58.7750428Z 2025-03-04T21:06:58.7750872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:06:58.7751418Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:06:58.7751654Z 2025-03-04T21:07:01.0397812Z 2025-03-04T21:07:01.0398846Z class GraphModule(torch.nn.Module): 2025-03-04T21:07:01.0399275Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T21:07:01.0399615Z l_scores_0_ = L_scores_0_ 2025-03-04T21:07:01.0399830Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:07:01.0400028Z 2025-03-04T21:07:01.0400702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:07:01.0402252Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:07:01.0402595Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:07:01.0402932Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:07:01.0403259Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:07:01.0403563Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:07:01.0403812Z 2025-03-04T21:07:01.0404268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:07:01.0404802Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:07:01.0405044Z 2025-03-04T21:07:01.0405306Z 2025-03-04T21:07:01.0405525Z class GraphModule(torch.nn.Module): 2025-03-04T21:07:01.0405957Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T21:07:01.0406294Z l_scores_0_ = L_scores_0_ 2025-03-04T21:07:01.0406504Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:07:01.0406694Z 2025-03-04T21:07:01.0407251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:07:01.0407914Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:07:01.0408237Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:07:01.0408553Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:07:01.0408872Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:07:01.0409165Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:07:01.0409404Z 2025-03-04T21:07:01.0409854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:07:01.0410381Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:07:01.0410623Z 2025-03-04T21:07:18.9693493Z Compilation time (from dynamo_timed): 34.506594546 2025-03-04T21:07:18.9695203Z pass 2025-03-04T21:07:18.9696512Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:07:18.9697482Z TIMING: entire_frame_compile:34.50659 gc:0.03761 _recursive_pre_grad_passes:0.0297 async_compile.wait:9.05835 backend_compile:23.22984 _recursive_joint_graph_passes:0.16634 _recursive_post_grad_passes:0.07436 code_gen:12.06795 inductor_compile:13.81027 total_wall_time:34.50659 2025-03-04T21:07:18.9701072Z STATS: call_* op count: 607 | FakeTensorMode.__torch_dispatch__:17713 | FakeTensor.__torch_dispatch__:1776 | ProxyTorchDispatchMode.__torch_dispatch__:5612 | attempt fast:51 | slow no contiguity match:20 | fast is_contiguous:31 2025-03-04T21:07:18.9703176Z Dynamo produced 53 graphs covering 607 ops with 42 graph breaks (6 unique) 2025-03-04T21:07:24.6943712Z 2025-03-04T21:07:35.9802255Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:07:35.9802786Z loading model: 0it [00:11, ?it/s] 2025-03-04T21:07:35.9812299Z cpu eval detectron2_fasterrcnn_r_50_dc5 2025-03-04T21:07:49.9119021Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_50_dc5. Setting accuracy check to cosine 2025-03-04T21:07:49.9558128Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:08:03.9557230Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:08:17.8832679Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:08:28.0824868Z 2025-03-04T21:08:28.0826187Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:28.0883455Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", 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L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:08:28.0936237Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:08:28.0936758Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.0937573Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.0938420Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.0939249Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.0940054Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.0940864Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.0941679Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.0942547Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.0943502Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.0944334Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.0945146Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.0945971Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.0946884Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.0947738Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.0948520Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.0949242Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.0949945Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.0950707Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.0951445Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.0952152Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.0952839Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:28.0953646Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.0954524Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.0955386Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:28.0956196Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:28.0956880Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.0957584Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.0958379Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.0959153Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.0959873Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.0960561Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.0961276Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.0962038Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.0962755Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.0963446Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.0964090Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.0964762Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.0965482Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.0966197Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.0966890Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.0967551Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.0968244Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.0968989Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.0969708Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.0996379Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.0997118Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.0997827Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.0998807Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.0999608Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1000365Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1001099Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1001783Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1002537Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1003264Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1003971Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1004662Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1005361Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1006092Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1006792Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1007473Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1008118Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1008794Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1009517Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1010220Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1010906Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1011549Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1012243Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1012974Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1013698Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1014406Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1015212Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:28.1015965Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1016743Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1017501Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1018241Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1018921Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1019616Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1020365Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1021085Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1021777Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1022432Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1023121Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1023861Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1024576Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1025270Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1025946Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1026630Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1027395Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1028133Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1028839Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1029493Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1030173Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1030910Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1031621Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1032316Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1033041Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1033715Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1034436Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1035134Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1035813Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1036455Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1037126Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1037844Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1038544Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1039221Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1039880Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1040545Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1041288Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1042010Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1042704Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1043346Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1044018Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1046265Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1047074Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1047773Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1048441Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1049142Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1049891Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1050625Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1051328Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1051995Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1052709Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1053460Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1054183Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1055023Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1055711Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1056451Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1057238Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1058016Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1058718Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1060626Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1061441Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1062201Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1062929Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1063635Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1064321Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:28.1065049Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1065820Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1066578Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1067296Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1067959Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1068640Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1069367Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1070078Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1070799Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1071467Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1072160Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1072906Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1073654Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1074349Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1075018Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1075726Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1076462Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1077184Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1077863Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1078511Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1079205Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1079949Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1082797Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1083521Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1084189Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1084889Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1085635Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1086399Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1087106Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1087790Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1088696Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1089489Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1090208Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1090904Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1091584Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1092299Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1093059Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1093806Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1094538Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1095313Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1096038Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1096790Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1097523Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1098225Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1098895Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1099612Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1100433Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1101177Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1101963Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1102656Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1103380Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1104153Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1104892Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1105602Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1106277Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1107047Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1107818Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1108561Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1109284Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1109991Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1110709Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1111486Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1112220Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1112939Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1113616Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1114331Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1115114Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1115861Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1116619Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1117315Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1118048Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1118792Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1119518Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1120214Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1120878Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1121568Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1122336Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1123065Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1123766Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1124432Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1125129Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1125875Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1126595Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1127289Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1127952Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1128686Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1129461Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1130226Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1130962Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1131678Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1132416Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1133197Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1133953Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1134767Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1135537Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:28.1136347Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1137169Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1137964Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1138730Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1139447Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1140180Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1140964Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1141724Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1142454Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1143147Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1143896Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1144699Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1145478Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1146205Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1146921Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1147658Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1148455Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1149219Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1149961Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1150664Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1151414Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1152186Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1152926Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1153646Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1154326Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1155043Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1163206Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1163984Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1164689Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1165421Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1166112Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1166894Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1167647Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1168365Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1169093Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:08:28.1169826Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:08:28.1170524Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:08:28.1171271Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:08:28.1172067Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:08:28.1172865Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:08:28.1173656Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:08:28.1174152Z 2025-03-04T21:08:28.1174649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1175542Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1176233Z 2025-03-04T21:08:28.1176656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1178622Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1180362Z 2025-03-04T21:08:28.1180824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:08:28.1181411Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:08:28.1181732Z 2025-03-04T21:08:28.1182244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:08:28.1182998Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:08:28.1183415Z 2025-03-04T21:08:28.1183811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1184650Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1185262Z 2025-03-04T21:08:28.1185673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1187750Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1189829Z 2025-03-04T21:08:28.1190262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1190813Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:08:28.1191110Z 2025-03-04T21:08:28.1191473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1192226Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1192777Z 2025-03-04T21:08:28.1193134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1195083Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1196743Z 2025-03-04T21:08:28.1197118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1197608Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:08:28.1197871Z 2025-03-04T21:08:28.1198245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1199072Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1199628Z 2025-03-04T21:08:28.1199985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1201906Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1203568Z 2025-03-04T21:08:28.1203922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1204686Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:28.1205253Z 2025-03-04T21:08:28.1205615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1207569Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1209239Z 2025-03-04T21:08:28.1209604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1210104Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:08:28.1210369Z 2025-03-04T21:08:28.1210732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1211237Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:08:28.1211511Z 2025-03-04T21:08:28.1211854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1212606Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1213169Z 2025-03-04T21:08:28.1213531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1215694Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1217468Z 2025-03-04T21:08:28.1217851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1218344Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:08:28.1218614Z 2025-03-04T21:08:28.1218958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1219714Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1220277Z 2025-03-04T21:08:28.1220634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1222500Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1224122Z 2025-03-04T21:08:28.1224518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1224994Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:08:28.1225277Z 2025-03-04T21:08:28.1225612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1226363Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1226925Z 2025-03-04T21:08:28.1227271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1229121Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1230752Z 2025-03-04T21:08:28.1231125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1231620Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:08:28.1231898Z 2025-03-04T21:08:28.1232274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1232767Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:08:28.1233041Z 2025-03-04T21:08:28.1233381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1234116Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1234659Z 2025-03-04T21:08:28.1235010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1236879Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1238509Z 2025-03-04T21:08:28.1238883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1239387Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:08:28.1239649Z 2025-03-04T21:08:28.1240007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1240756Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1241303Z 2025-03-04T21:08:28.1241657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1243526Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1245159Z 2025-03-04T21:08:28.1245533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1246020Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:08:28.1246280Z 2025-03-04T21:08:28.1246618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1247364Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1247926Z 2025-03-04T21:08:28.1248281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1250163Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1251818Z 2025-03-04T21:08:28.1252221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1252720Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:08:28.1253017Z 2025-03-04T21:08:28.1253393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1253917Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:08:28.1254212Z 2025-03-04T21:08:28.1258505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1259470Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1260043Z 2025-03-04T21:08:28.1260421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1262351Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1263976Z 2025-03-04T21:08:28.1264353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1264842Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:08:28.1265105Z 2025-03-04T21:08:28.1265443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1266187Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1266733Z 2025-03-04T21:08:28.1267081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1269003Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1270598Z 2025-03-04T21:08:28.1270966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1271485Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:08:28.1271753Z 2025-03-04T21:08:28.1272104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1272870Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1273415Z 2025-03-04T21:08:28.1273760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1275591Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1277182Z 2025-03-04T21:08:28.1277514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1278256Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:28.1278820Z 2025-03-04T21:08:28.1279177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1281106Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1282857Z 2025-03-04T21:08:28.1283226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1283716Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:08:28.1284005Z 2025-03-04T21:08:28.1284387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1284920Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:08:28.1285191Z 2025-03-04T21:08:28.1285535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1286315Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1286920Z 2025-03-04T21:08:28.1287274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1291321Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1293251Z 2025-03-04T21:08:28.1293712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1294248Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:08:28.1294540Z 2025-03-04T21:08:28.1295040Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1295882Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1296485Z 2025-03-04T21:08:28.1296883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1298841Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1300543Z 2025-03-04T21:08:28.1301135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1301659Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:08:28.1301985Z 2025-03-04T21:08:28.1302342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1303164Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1303772Z 2025-03-04T21:08:28.1304143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1306062Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1307723Z 2025-03-04T21:08:28.1308104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1308615Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:08:28.1308894Z 2025-03-04T21:08:28.1309274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1309777Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:08:28.1310055Z 2025-03-04T21:08:28.1310399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1311142Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1311688Z 2025-03-04T21:08:28.1312042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1313897Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1315546Z 2025-03-04T21:08:28.1315934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1316467Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:08:28.1316747Z 2025-03-04T21:08:28.1317094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1317875Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1318457Z 2025-03-04T21:08:28.1318818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1320699Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1322376Z 2025-03-04T21:08:28.1322759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1323260Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:08:28.1323528Z 2025-03-04T21:08:28.1323875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1324633Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1325192Z 2025-03-04T21:08:28.1325549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1327423Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1329053Z 2025-03-04T21:08:28.1329448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1329933Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:08:28.1330231Z 2025-03-04T21:08:28.1330602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1331092Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:08:28.1331385Z 2025-03-04T21:08:28.1331749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1332496Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1333038Z 2025-03-04T21:08:28.1333395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1335550Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1337303Z 2025-03-04T21:08:28.1337686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1338188Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:08:28.1338456Z 2025-03-04T21:08:28.1338803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1339557Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1340118Z 2025-03-04T21:08:28.1340475Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1342374Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1344035Z 2025-03-04T21:08:28.1344399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1344935Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:08:28.1345207Z 2025-03-04T21:08:28.1345576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1346359Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1346929Z 2025-03-04T21:08:28.1347302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1349290Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1351043Z 2025-03-04T21:08:28.1351426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1351940Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:08:28.1352224Z 2025-03-04T21:08:28.1352609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1353125Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:08:28.1353411Z 2025-03-04T21:08:28.1353766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1354526Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1355083Z 2025-03-04T21:08:28.1355454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1357416Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1359080Z 2025-03-04T21:08:28.1359455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1359965Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:08:28.1360248Z 2025-03-04T21:08:28.1360588Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1361334Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1361884Z 2025-03-04T21:08:28.1362243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1364102Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1365732Z 2025-03-04T21:08:28.1366111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1366596Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:08:28.1366859Z 2025-03-04T21:08:28.1367203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1367949Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1368500Z 2025-03-04T21:08:28.1368858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1370683Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1372290Z 2025-03-04T21:08:28.1372629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1373404Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:28.1373978Z 2025-03-04T21:08:28.1374332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1376547Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1378304Z 2025-03-04T21:08:28.1378685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1379180Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:08:28.1379448Z 2025-03-04T21:08:28.1379829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1380328Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:08:28.1380598Z 2025-03-04T21:08:28.1380945Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1381674Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1382202Z 2025-03-04T21:08:28.1382552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1384355Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1385957Z 2025-03-04T21:08:28.1386362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1386855Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:08:28.1387110Z 2025-03-04T21:08:28.1387438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1389643Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1390296Z 2025-03-04T21:08:28.1390679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1392561Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1394211Z 2025-03-04T21:08:28.1394595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1395076Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:08:28.1395340Z 2025-03-04T21:08:28.1395681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1396417Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1396962Z 2025-03-04T21:08:28.1397317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1399188Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1400864Z 2025-03-04T21:08:28.1401246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1401800Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:08:28.1402083Z 2025-03-04T21:08:28.1402492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1402994Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:08:28.1403263Z 2025-03-04T21:08:28.1403622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1404396Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1404917Z 2025-03-04T21:08:28.1405259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1407062Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1408635Z 2025-03-04T21:08:28.1408997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1409468Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:08:28.1409729Z 2025-03-04T21:08:28.1410067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1410789Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1411316Z 2025-03-04T21:08:28.1411660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1413479Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1415181Z 2025-03-04T21:08:28.1415615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1416141Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:08:28.1416415Z 2025-03-04T21:08:28.1416779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1417539Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1418111Z 2025-03-04T21:08:28.1418474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1420349Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1421997Z 2025-03-04T21:08:28.1422374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1422870Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:08:28.1423144Z 2025-03-04T21:08:28.1423526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1424028Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:08:28.1424294Z 2025-03-04T21:08:28.1424642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1425387Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1425933Z 2025-03-04T21:08:28.1426294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1428204Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1429850Z 2025-03-04T21:08:28.1430305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1430792Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:08:28.1431052Z 2025-03-04T21:08:28.1431444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1432958Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1433504Z 2025-03-04T21:08:28.1433865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1435717Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1437350Z 2025-03-04T21:08:28.1437725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1438210Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:08:28.1438471Z 2025-03-04T21:08:28.1438812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1439552Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1440107Z 2025-03-04T21:08:28.1440483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1442455Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1444173Z 2025-03-04T21:08:28.1444595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1445134Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:08:28.1445417Z 2025-03-04T21:08:28.1445808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1446347Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:08:28.1446657Z 2025-03-04T21:08:28.1447019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1447796Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1448358Z 2025-03-04T21:08:28.1448736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1450684Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1452420Z 2025-03-04T21:08:28.1452819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1453330Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:08:28.1453608Z 2025-03-04T21:08:28.1453973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1454830Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1455442Z 2025-03-04T21:08:28.1455847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1457839Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1459579Z 2025-03-04T21:08:28.1460001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1460519Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:08:28.1460799Z 2025-03-04T21:08:28.1461185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1462992Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1463614Z 2025-03-04T21:08:28.1465320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1467572Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1470270Z 2025-03-04T21:08:28.1470670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1471177Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:08:28.1471440Z 2025-03-04T21:08:28.1471819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1472316Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:08:28.1472582Z 2025-03-04T21:08:28.1472924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1473665Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1474208Z 2025-03-04T21:08:28.1474591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1476513Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1478190Z 2025-03-04T21:08:28.1478566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1479073Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:08:28.1479358Z 2025-03-04T21:08:28.1479704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1480450Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1480994Z 2025-03-04T21:08:28.1481356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1483203Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1484836Z 2025-03-04T21:08:28.1485220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1485700Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:08:28.1485961Z 2025-03-04T21:08:28.1486302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1487044Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1487595Z 2025-03-04T21:08:28.1487952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1490634Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1492359Z 2025-03-04T21:08:28.1492787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1493313Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:08:28.1493599Z 2025-03-04T21:08:28.1494028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1494688Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:08:28.1494998Z 2025-03-04T21:08:28.1495383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1496668Z x_88: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1497249Z 2025-03-04T21:08:28.1497634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1499596Z x_89: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1501321Z 2025-03-04T21:08:28.1501710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1502221Z out_52: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:08:28.1502498Z 2025-03-04T21:08:28.1502866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1503584Z x_90: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_52 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1504109Z 2025-03-04T21:08:28.1504456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1506265Z x_91: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1507851Z 2025-03-04T21:08:28.1508227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1508732Z out_53: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:08:28.1509010Z 2025-03-04T21:08:28.1509362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1510090Z x_92: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1510625Z 2025-03-04T21:08:28.1510976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1512798Z x_93: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1514407Z 2025-03-04T21:08:28.1514738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1515475Z x_94: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_51 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:28.1516024Z 2025-03-04T21:08:28.1516363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1518251Z x_95: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1519955Z 2025-03-04T21:08:28.1520351Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1520824Z x_93 += x_95; out_54: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_93; x_93 = x_95 = None 2025-03-04T21:08:28.1521101Z 2025-03-04T21:08:28.1521464Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1521945Z out_55: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:08:28.1522209Z 2025-03-04T21:08:28.1522560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1523297Z x_96: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1523826Z 2025-03-04T21:08:28.1524178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1525984Z x_97: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1527575Z 2025-03-04T21:08:28.1527948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1528423Z out_56: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:08:28.1528679Z 2025-03-04T21:08:28.1529013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1529742Z x_98: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_56 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1530276Z 2025-03-04T21:08:28.1530628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1532483Z x_99: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1534140Z 2025-03-04T21:08:28.1534516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1535113Z out_57: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_99); x_99 = None 2025-03-04T21:08:28.1535394Z 2025-03-04T21:08:28.1535754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1536552Z x_100: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1537123Z 2025-03-04T21:08:28.1537477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1539343Z x_101: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1540977Z 2025-03-04T21:08:28.1541352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1541852Z x_101 += out_55; out_58: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_101; x_101 = out_55 = None 2025-03-04T21:08:28.1542132Z 2025-03-04T21:08:28.1542505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1542999Z out_59: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:08:28.1543267Z 2025-03-04T21:08:28.1543606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1544345Z x_102: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1544881Z 2025-03-04T21:08:28.1545234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1547101Z x_103: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1548758Z 2025-03-04T21:08:28.1549132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1549622Z out_60: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:08:28.1549895Z 2025-03-04T21:08:28.1550241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1550974Z x_104: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_60 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1551502Z 2025-03-04T21:08:28.1551846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1553636Z x_105: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1555217Z 2025-03-04T21:08:28.1555581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1556058Z out_61: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_105); x_105 = None 2025-03-04T21:08:28.1556311Z 2025-03-04T21:08:28.1556643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1557362Z x_106: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1557898Z 2025-03-04T21:08:28.1558244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1560061Z x_107: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1561665Z 2025-03-04T21:08:28.1562022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1562543Z x_107 += out_59; out_62: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_107; x_107 = out_59 = None 2025-03-04T21:08:28.1562812Z 2025-03-04T21:08:28.1563171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1563670Z out_63: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:08:28.1563957Z 2025-03-04T21:08:28.1564477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:28.1565111Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:08:28.1565381Z 2025-03-04T21:08:28.1565763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:28.1566244Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:08:28.1566497Z 2025-03-04T21:08:28.1567015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:28.1567645Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:08:28.1567909Z 2025-03-04T21:08:28.1568285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:28.1568763Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:08:28.1569021Z 2025-03-04T21:08:28.1569474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:28.1570068Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:08:28.1570399Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:08:28.1570668Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:08:28.1570901Z 2025-03-04T21:08:28.1571308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:28.1571810Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:08:28.1572050Z 2025-03-04T21:08:28.1572453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:28.1572951Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:08:28.1573193Z 2025-03-04T21:08:28.1573678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:28.1574329Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:08:28.1574739Z 2025-03-04T21:08:28.1575311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:28.1575954Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:08:28.1576628Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:08:28.1577251Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:08:28.1577573Z x_108: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:08:28.1577813Z 2025-03-04T21:08:28.1578207Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:28.1578695Z tensor: "f32[82125, 4][4, 1]cpu" = x_108.to(torch.float32); x_108 = None 2025-03-04T21:08:28.1578944Z 2025-03-04T21:08:28.1579290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1580396Z x_109: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_63 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:08:28.1581282Z 2025-03-04T21:08:28.1581669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:28.1582198Z x_110: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_109, inplace = False); x_109 = None 2025-03-04T21:08:28.1582511Z 2025-03-04T21:08:28.1582978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:28.1584264Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:08:28.1585226Z 2025-03-04T21:08:28.1585678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:28.1586911Z x_111: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_110 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:08:28.1587849Z 2025-03-04T21:08:28.1589398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:08:28.1590009Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:08:28.1590442Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:08:28.1590713Z 2025-03-04T21:08:28.1591233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:08:28.1591887Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_111.view(4, -1, 4, 73, 75); x_111 = None 2025-03-04T21:08:28.1592305Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:08:28.1592731Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:08:28.1593026Z 2025-03-04T21:08:28.1593530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:28.1594193Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:08:28.1594515Z 2025-03-04T21:08:28.1595039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:28.1595678Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:08:28.1596034Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:08:28.1596374Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:08:28.1596632Z 2025-03-04T21:08:28.1597098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:28.1597694Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:08:28.1597988Z 2025-03-04T21:08:28.1598390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:28.1598904Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:08:28.1599167Z 2025-03-04T21:08:28.1599569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:28.1600074Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:28.1600389Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:28.1600717Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:08:28.1600986Z 2025-03-04T21:08:28.1601402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:28.1601902Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:28.1602207Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:28.1602532Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:08:28.1602802Z 2025-03-04T21:08:28.1603203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:28.1603737Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:28.1604010Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:08:28.1604277Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:08:28.1604542Z 2025-03-04T21:08:28.1604947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:28.1605474Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:28.1605781Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:08:28.1606070Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:08:28.1606313Z 2025-03-04T21:08:28.1606745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:28.1607257Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:28.1607585Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:08:28.1607824Z 2025-03-04T21:08:28.1608219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:28.1608725Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:28.1609051Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:08:28.1609281Z 2025-03-04T21:08:28.1609671Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:28.1610184Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:28.1610503Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:08:28.1610735Z 2025-03-04T21:08:28.1611125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:28.1611668Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:28.1612017Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:08:28.1612252Z 2025-03-04T21:08:28.1612686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:28.1613226Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:28.1613491Z 2025-03-04T21:08:28.1613922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:28.1614455Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:28.1614791Z 2025-03-04T21:08:28.1615244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:28.1615816Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:28.1616143Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:08:28.1616481Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:28.1616839Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:08:28.1617149Z 2025-03-04T21:08:28.1617599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:28.1618165Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:28.1618485Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:08:28.1618817Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:28.1619185Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:08:28.1619464Z 2025-03-04T21:08:28.1619889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:28.1620399Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:28.1620732Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:28.1621088Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:08:28.1621342Z 2025-03-04T21:08:28.1621772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:28.1622284Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:28.1622630Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:28.1622991Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:08:28.1623249Z 2025-03-04T21:08:28.1623658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:28.1624129Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:28.1624402Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:28.1624641Z 2025-03-04T21:08:28.1625043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:28.1625508Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:28.1625772Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:28.1626011Z 2025-03-04T21:08:28.1626409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:28.1626890Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:28.1627182Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:28.1627432Z 2025-03-04T21:08:28.1627832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:28.1628309Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:28.1628613Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:28.1628864Z 2025-03-04T21:08:28.1629301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:28.1629888Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:28.1630183Z 2025-03-04T21:08:28.1630644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:28.1631216Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:08:28.1631500Z 2025-03-04T21:08:28.1631981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:28.1632620Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:08:28.1632937Z 2025-03-04T21:08:28.1633517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:08:28.1634217Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:08:28.1634478Z 2025-03-04T21:08:28.1634871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:28.1635373Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:08:28.1635641Z 2025-03-04T21:08:28.1636180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:28.1636800Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:08:28.1637082Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:08:28.1637361Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:08:28.1637598Z 2025-03-04T21:08:28.1638162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:28.1638854Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:08:28.1639319Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:08:28.1639677Z 2025-03-04T21:08:28.1640235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:28.1640917Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:28.1641208Z 2025-03-04T21:08:28.1641601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:28.1642114Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:08:28.1642394Z 2025-03-04T21:08:28.1642876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:08:28.1643468Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:08:28.1643748Z 2025-03-04T21:08:28.1644142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:28.1644675Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:08:28.1644945Z 2025-03-04T21:08:28.1645403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:08:28.1645988Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:08:28.1646245Z 2025-03-04T21:08:28.1646831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:08:28.1647523Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:08:28.1647843Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:28.1648178Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:08:28.1648521Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:08:28.1648777Z 2025-03-04T21:08:28.1649236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:08:28.1649778Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:08:28.1650016Z 2025-03-04T21:08:28.1650446Z 2025-03-04T21:08:28.1650550Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:28.1703457Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: 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"f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:08:28.1754764Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:08:28.1755248Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1756050Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1756849Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1757553Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1758225Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1758912Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1759636Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1760377Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1761125Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1761855Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1762508Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1763208Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1763956Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1764670Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1765363Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1766021Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1766712Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1767452Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1768165Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1768857Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1769532Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:28.1770249Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1771020Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1771764Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1772506Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1773181Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1773906Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1774719Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1775482Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1776195Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1776860Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1777558Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1777904Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1778238Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1778556Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1778858Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1779205Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1779558Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1779884Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1780212Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1780512Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1780863Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1781212Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1781559Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1781884Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1782187Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1782555Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1782922Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1783260Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1783585Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1783875Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1784234Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1784581Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1784916Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1785232Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1785534Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1785890Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1786263Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1786616Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1786957Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1787277Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1787643Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1788001Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1789352Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1789740Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1790079Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1790494Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1790890Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1791236Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1791579Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1791901Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:28.1792300Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1792681Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1793050Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1793410Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1793718Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1794091Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1794451Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1794796Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1795130Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1795445Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1795811Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1796196Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1796547Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1796900Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1797223Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1797604Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1797968Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1798308Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1798647Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1798952Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1799326Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1799694Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1800032Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1800370Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1800658Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1801008Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1801346Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1801679Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1801994Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1802294Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1802640Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1803011Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1803358Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1803685Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1803982Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1804341Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1804702Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1805023Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1805348Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1805643Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1805989Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1806332Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1806654Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1806977Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1807266Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1807620Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1807959Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1808292Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1808615Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1808905Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1809278Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1809619Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1809965Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1810296Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1810610Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1810956Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1811305Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1811638Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1811955Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1812252Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1812599Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1812946Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1813270Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1813594Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1813898Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:28.1814270Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1814784Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1815154Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1815512Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1815828Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1816210Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1816566Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1816916Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1817250Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1817548Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1817905Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1818250Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1818584Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1818902Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1819202Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1819548Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1819898Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1820223Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1820548Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1820848Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1821196Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1821548Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1821874Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1822202Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1822513Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1822865Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1823222Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1823570Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1823917Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1824205Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1824556Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1824898Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1825230Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1825545Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1825840Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1826183Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1826530Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1826858Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1827172Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1827467Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1827811Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1828155Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1828474Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1828837Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1829124Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1829492Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1829852Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1830190Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1830511Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1830796Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1831832Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1832195Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1832530Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1832844Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1833142Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1833494Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1833837Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1834169Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1834485Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1834782Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1835130Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1835484Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1835811Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1836163Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1836478Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1836826Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1837192Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1837528Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1837850Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1838136Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1838495Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1838838Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1839169Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1839489Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1839776Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1840131Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1840475Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1840807Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1841121Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1841416Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1841764Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1842118Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1842465Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1842785Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1843096Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1843462Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1843831Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1844163Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1844485Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1844772Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1845131Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1845479Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1845804Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1846129Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1846435Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:28.1846804Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1847159Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1847508Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1847838Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1848133Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1848488Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1848829Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1849178Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1849512Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1849826Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1850183Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1850533Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1850857Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1851186Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1851497Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1851844Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1852199Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1852527Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1852856Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1853153Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.1853516Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1853862Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1854199Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1854537Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1854904Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.1855275Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1855660Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1856003Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1856343Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1856661Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.1857034Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.1857392Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.1857730Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.1858052Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.1858425Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:08:28.1858758Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:08:28.1859092Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:08:28.1859481Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:08:28.1859864Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:08:28.1860232Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:08:28.1860602Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:08:28.1860687Z 2025-03-04T21:08:28.1860987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1861497Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1861569Z 2025-03-04T21:08:28.1861866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1863417Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1863526Z 2025-03-04T21:08:28.1863862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:08:28.1864016Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:08:28.1864092Z 2025-03-04T21:08:28.1864483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:08:28.1864750Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:08:28.1864819Z 2025-03-04T21:08:28.1865104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1865561Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1865644Z 2025-03-04T21:08:28.1865932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1867568Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1867647Z 2025-03-04T21:08:28.1867953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1868101Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:08:28.1868167Z 2025-03-04T21:08:28.1868432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1868890Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1868962Z 2025-03-04T21:08:28.1869232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1870812Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1870914Z 2025-03-04T21:08:28.1871208Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1871357Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:08:28.1871422Z 2025-03-04T21:08:28.1871683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1872126Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1872201Z 2025-03-04T21:08:28.1872465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1873989Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1874064Z 2025-03-04T21:08:28.1874319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1874764Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:28.1874832Z 2025-03-04T21:08:28.1875105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1876701Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1876801Z 2025-03-04T21:08:28.1877092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1877974Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:08:28.1878074Z 2025-03-04T21:08:28.1878365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1878527Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:08:28.1878593Z 2025-03-04T21:08:28.1878856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1879280Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1879353Z 2025-03-04T21:08:28.1879623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1881182Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1881261Z 2025-03-04T21:08:28.1881552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1881701Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:08:28.1881765Z 2025-03-04T21:08:28.1882026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1882455Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1882529Z 2025-03-04T21:08:28.1882797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1884324Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1884413Z 2025-03-04T21:08:28.1884718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1884881Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:08:28.1884946Z 2025-03-04T21:08:28.1885202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1885637Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1885709Z 2025-03-04T21:08:28.1885972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1887506Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1887581Z 2025-03-04T21:08:28.1887863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1888026Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:08:28.1889065Z 2025-03-04T21:08:28.1889458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1889629Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:08:28.1889706Z 2025-03-04T21:08:28.1889963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1890403Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1890469Z 2025-03-04T21:08:28.1890744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1892467Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1893234Z 2025-03-04T21:08:28.1893725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1893923Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:08:28.1894007Z 2025-03-04T21:08:28.1894355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1895009Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1895104Z 2025-03-04T21:08:28.1895465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1897210Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1897303Z 2025-03-04T21:08:28.1897695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1897865Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:08:28.1897957Z 2025-03-04T21:08:28.1898296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1898840Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1898917Z 2025-03-04T21:08:28.1899227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1901098Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1901229Z 2025-03-04T21:08:28.1901607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1901783Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:08:28.1901863Z 2025-03-04T21:08:28.1902192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1902359Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:08:28.1902424Z 2025-03-04T21:08:28.1902688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1903138Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1903206Z 2025-03-04T21:08:28.1903487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1905067Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1905147Z 2025-03-04T21:08:28.1905437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1905594Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:08:28.1905666Z 2025-03-04T21:08:28.1905923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1906379Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1906447Z 2025-03-04T21:08:28.1906722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1908326Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1908435Z 2025-03-04T21:08:28.1908733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1908882Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:08:28.1908954Z 2025-03-04T21:08:28.1909214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1909667Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1909735Z 2025-03-04T21:08:28.1910010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1911569Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1911647Z 2025-03-04T21:08:28.1911909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1912364Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:28.1912437Z 2025-03-04T21:08:28.1912708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1914370Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1914464Z 2025-03-04T21:08:28.1914752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1914931Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:08:28.1915013Z 2025-03-04T21:08:28.1915312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1915469Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:08:28.1915543Z 2025-03-04T21:08:28.1915803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1916246Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1916315Z 2025-03-04T21:08:28.1916588Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1918146Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1918225Z 2025-03-04T21:08:28.1918523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1918666Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:08:28.1918737Z 2025-03-04T21:08:28.1918996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1919440Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1919509Z 2025-03-04T21:08:28.1919793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1921363Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1921456Z 2025-03-04T21:08:28.1921769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1921983Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:08:28.1922061Z 2025-03-04T21:08:28.1922318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1922763Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1922829Z 2025-03-04T21:08:28.1923110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1924680Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1924754Z 2025-03-04T21:08:28.1925048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1925210Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:08:28.1925283Z 2025-03-04T21:08:28.1925570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1925735Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:08:28.1925800Z 2025-03-04T21:08:28.1926066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1926498Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1926573Z 2025-03-04T21:08:28.1926847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1928420Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1928538Z 2025-03-04T21:08:28.1928829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1928986Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:08:28.1929051Z 2025-03-04T21:08:28.1929312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1929748Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1929823Z 2025-03-04T21:08:28.1930092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1931622Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1931702Z 2025-03-04T21:08:28.1931995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1932145Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:08:28.1932211Z 2025-03-04T21:08:28.1932472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1932908Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1932982Z 2025-03-04T21:08:28.1933255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1934901Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1935005Z 2025-03-04T21:08:28.1935309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1935563Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:08:28.1935633Z 2025-03-04T21:08:28.1935956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1936109Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:08:28.1936186Z 2025-03-04T21:08:28.1936438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1936877Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1936947Z 2025-03-04T21:08:28.1937223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1938774Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1938846Z 2025-03-04T21:08:28.1939143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1939288Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:08:28.1939363Z 2025-03-04T21:08:28.1939618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1940057Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1940133Z 2025-03-04T21:08:28.1940399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1942002Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1942096Z 2025-03-04T21:08:28.1942400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1942543Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:08:28.1942615Z 2025-03-04T21:08:28.1942867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1943304Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1943379Z 2025-03-04T21:08:28.1943646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1945175Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1945242Z 2025-03-04T21:08:28.1945535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1945696Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:08:28.1945767Z 2025-03-04T21:08:28.1946052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1946212Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:08:28.1946285Z 2025-03-04T21:08:28.1946534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1946965Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1947031Z 2025-03-04T21:08:28.1947319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1948847Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1948951Z 2025-03-04T21:08:28.1949251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1949392Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:08:28.1949471Z 2025-03-04T21:08:28.1949728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1950167Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1950238Z 2025-03-04T21:08:28.1950517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1952046Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1952124Z 2025-03-04T21:08:28.1952423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1952567Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:08:28.1952644Z 2025-03-04T21:08:28.1952900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1953343Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1953413Z 2025-03-04T21:08:28.1953692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1955266Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1955379Z 2025-03-04T21:08:28.1955638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1956074Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:28.1956151Z 2025-03-04T21:08:28.1956418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1958007Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1958083Z 2025-03-04T21:08:28.1958366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1958519Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:08:28.1958586Z 2025-03-04T21:08:28.1958881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1959031Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:08:28.1959103Z 2025-03-04T21:08:28.1959357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1959789Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1959855Z 2025-03-04T21:08:28.1960130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1961688Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1961791Z 2025-03-04T21:08:28.1962085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1962221Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:08:28.1962294Z 2025-03-04T21:08:28.1962547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1962980Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1963046Z 2025-03-04T21:08:28.1963319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1964830Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1964903Z 2025-03-04T21:08:28.1965202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1965340Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:08:28.1965411Z 2025-03-04T21:08:28.1965662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1966095Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1966163Z 2025-03-04T21:08:28.1966438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1967982Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1968063Z 2025-03-04T21:08:28.1968366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1968529Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:08:28.1968602Z 2025-03-04T21:08:28.1968887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1969040Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:08:28.1969105Z 2025-03-04T21:08:28.1969369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1969791Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1969863Z 2025-03-04T21:08:28.1970128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1971637Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1971712Z 2025-03-04T21:08:28.1972001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1972142Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:08:28.1972205Z 2025-03-04T21:08:28.1972467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1972883Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1972956Z 2025-03-04T21:08:28.1973221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1975011Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1975128Z 2025-03-04T21:08:28.1975429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1975576Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:08:28.1975646Z 2025-03-04T21:08:28.1975919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1976365Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1976445Z 2025-03-04T21:08:28.1976724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1978328Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1978408Z 2025-03-04T21:08:28.1978705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1978873Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:08:28.1978944Z 2025-03-04T21:08:28.1979256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1979407Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:08:28.1979487Z 2025-03-04T21:08:28.1979751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1980201Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1980270Z 2025-03-04T21:08:28.1980556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1982202Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1982339Z 2025-03-04T21:08:28.1982654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1982799Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:08:28.1982877Z 2025-03-04T21:08:28.1983147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1983602Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1983673Z 2025-03-04T21:08:28.1983963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1985533Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1985602Z 2025-03-04T21:08:28.1985897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1986033Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:08:28.1986107Z 2025-03-04T21:08:28.1986361Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1986795Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1986903Z 2025-03-04T21:08:28.1987172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1989214Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1989335Z 2025-03-04T21:08:28.1989628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1989777Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:08:28.1989855Z 2025-03-04T21:08:28.1990143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1990298Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:08:28.1990363Z 2025-03-04T21:08:28.1990624Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1991046Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.1991114Z 2025-03-04T21:08:28.1991388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1992899Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1992977Z 2025-03-04T21:08:28.1993263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1993406Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:08:28.1993471Z 2025-03-04T21:08:28.1993728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1994159Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.1994226Z 2025-03-04T21:08:28.1994498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1996038Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1996143Z 2025-03-04T21:08:28.1996430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.1996573Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:08:28.1996648Z 2025-03-04T21:08:28.1996904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.1997339Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.1997406Z 2025-03-04T21:08:28.1997678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.1999193Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.1999268Z 2025-03-04T21:08:28.1999559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.1999707Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:08:28.1999778Z 2025-03-04T21:08:28.2000061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2000209Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:08:28.2000274Z 2025-03-04T21:08:28.2000533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2000949Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2001021Z 2025-03-04T21:08:28.2001308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2002855Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2002967Z 2025-03-04T21:08:28.2003255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2003396Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:08:28.2003461Z 2025-03-04T21:08:28.2003723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2004147Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2004221Z 2025-03-04T21:08:28.2004487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2005999Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2006074Z 2025-03-04T21:08:28.2006357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2006497Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:08:28.2006565Z 2025-03-04T21:08:28.2006823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2007244Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2007316Z 2025-03-04T21:08:28.2007581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2009142Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2009248Z 2025-03-04T21:08:28.2009531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2009687Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:08:28.2009754Z 2025-03-04T21:08:28.2010048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2010190Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:08:28.2010263Z 2025-03-04T21:08:28.2010517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2010940Z x_88: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2011006Z 2025-03-04T21:08:28.2011281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2012799Z x_89: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2012874Z 2025-03-04T21:08:28.2013169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2013306Z out_52: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:08:28.2013378Z 2025-03-04T21:08:28.2013630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2014061Z x_90: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_52 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2014125Z 2025-03-04T21:08:28.2014431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2025729Z x_91: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2025926Z 2025-03-04T21:08:28.2026283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2026432Z out_53: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:08:28.2026509Z 2025-03-04T21:08:28.2026782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2027272Z x_92: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2027340Z 2025-03-04T21:08:28.2027703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2029286Z x_93: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2029356Z 2025-03-04T21:08:28.2029635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2030142Z x_94: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_51 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:28.2030220Z 2025-03-04T21:08:28.2030507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2032196Z x_95: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2032290Z 2025-03-04T21:08:28.2032594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2032767Z x_93 += x_95; out_54: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_93; x_93 = x_95 = None 2025-03-04T21:08:28.2032834Z 2025-03-04T21:08:28.2033176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2033327Z out_55: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:08:28.2033402Z 2025-03-04T21:08:28.2033660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2034104Z x_96: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2034174Z 2025-03-04T21:08:28.2034453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2036041Z x_97: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2036112Z 2025-03-04T21:08:28.2036420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2036558Z out_56: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:08:28.2036633Z 2025-03-04T21:08:28.2036887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2037325Z x_98: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_56 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2037389Z 2025-03-04T21:08:28.2037652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2039195Z x_99: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2039302Z 2025-03-04T21:08:28.2039588Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2039717Z out_57: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_99); x_99 = None 2025-03-04T21:08:28.2039790Z 2025-03-04T21:08:28.2040035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2040468Z x_100: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2040531Z 2025-03-04T21:08:28.2040800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2043695Z x_101: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2043790Z 2025-03-04T21:08:28.2044120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2044286Z x_101 += out_55; out_58: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_101; x_101 = out_55 = None 2025-03-04T21:08:28.2044360Z 2025-03-04T21:08:28.2044649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2044805Z out_59: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:08:28.2044872Z 2025-03-04T21:08:28.2045141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2045556Z x_102: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2045632Z 2025-03-04T21:08:28.2045906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2047509Z x_103: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2047624Z 2025-03-04T21:08:28.2047913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2048062Z out_60: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:08:28.2048129Z 2025-03-04T21:08:28.2048413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2048855Z x_104: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_60 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2048921Z 2025-03-04T21:08:28.2049199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2050760Z x_105: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2050839Z 2025-03-04T21:08:28.2051127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2051277Z out_61: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_105); x_105 = None 2025-03-04T21:08:28.2051345Z 2025-03-04T21:08:28.2051608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2052061Z x_106: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2052128Z 2025-03-04T21:08:28.2052403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2053985Z x_107: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2054121Z 2025-03-04T21:08:28.2054413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2054713Z x_107 += out_59; out_62: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_107; x_107 = out_59 = None 2025-03-04T21:08:28.2054799Z 2025-03-04T21:08:28.2055106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2055270Z out_63: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:08:28.2055339Z 2025-03-04T21:08:28.2055820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:28.2055988Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:08:28.2056068Z 2025-03-04T21:08:28.2056388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:28.2056545Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:08:28.2056615Z 2025-03-04T21:08:28.2057088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:28.2057243Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:08:28.2057317Z 2025-03-04T21:08:28.2057617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:28.2057768Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:08:28.2057832Z 2025-03-04T21:08:28.2058228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:28.2058411Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:08:28.2058521Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:08:28.2058644Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:08:28.2058715Z 2025-03-04T21:08:28.2059051Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:28.2059190Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:08:28.2059255Z 2025-03-04T21:08:28.2059593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:28.2059733Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:08:28.2059805Z 2025-03-04T21:08:28.2060189Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:28.2060453Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:08:28.2060517Z 2025-03-04T21:08:28.2060966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:28.2061111Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:08:28.2061545Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:08:28.2061680Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:08:28.2061797Z x_108: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:08:28.2061862Z 2025-03-04T21:08:28.2062168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:28.2062304Z tensor: "f32[82125, 4][4, 1]cpu" = x_108.to(torch.float32); x_108 = None 2025-03-04T21:08:28.2062370Z 2025-03-04T21:08:28.2062629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2063395Z x_109: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_63 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:08:28.2063469Z 2025-03-04T21:08:28.2063742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:28.2063938Z x_110: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_109, inplace = False); x_109 = None 2025-03-04T21:08:28.2064003Z 2025-03-04T21:08:28.2064394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:28.2065250Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:08:28.2065322Z 2025-03-04T21:08:28.2065692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:28.2066520Z x_111: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_110 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:08:28.2066607Z 2025-03-04T21:08:28.2066949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:08:28.2067135Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:08:28.2067293Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:08:28.2067364Z 2025-03-04T21:08:28.2067790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:08:28.2067958Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_111.view(4, -1, 4, 73, 75); x_111 = None 2025-03-04T21:08:28.2068133Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:08:28.2068319Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:08:28.2068384Z 2025-03-04T21:08:28.2068804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:28.2069013Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:08:28.2069084Z 2025-03-04T21:08:28.2069529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:28.2069689Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:08:28.2069846Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:08:28.2069984Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:08:28.2070058Z 2025-03-04T21:08:28.2070440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:28.2070620Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:08:28.2070685Z 2025-03-04T21:08:28.2071017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:28.2071159Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:08:28.2071232Z 2025-03-04T21:08:28.2071553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:28.2071693Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:28.2071822Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:28.2071978Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:08:28.2072042Z 2025-03-04T21:08:28.2072372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:28.2072510Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:28.2072640Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:28.2072805Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:08:28.2072877Z 2025-03-04T21:08:28.2073190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:28.2073334Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:28.2073443Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:08:28.2073575Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:08:28.2073639Z 2025-03-04T21:08:28.2073959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:28.2074105Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:28.2074206Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:08:28.2074335Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:08:28.2074407Z 2025-03-04T21:08:28.2074757Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:28.2074920Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:28.2075037Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:08:28.2075110Z 2025-03-04T21:08:28.2075423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:28.2075585Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:28.2075700Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:08:28.2075776Z 2025-03-04T21:08:28.2076073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:28.2076240Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:28.2076354Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:08:28.2076426Z 2025-03-04T21:08:28.2076726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:28.2076921Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:28.2077043Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:08:28.2077109Z 2025-03-04T21:08:28.2077463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:28.2077605Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:28.2077676Z 2025-03-04T21:08:28.2078016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:28.2078161Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:28.2078225Z 2025-03-04T21:08:28.2078595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:28.2078752Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:28.2078882Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:08:28.2079034Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:28.2079196Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:08:28.2079277Z 2025-03-04T21:08:28.2079630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:28.2079765Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:28.2079897Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:08:28.2080047Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:28.2080189Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:08:28.2080254Z 2025-03-04T21:08:28.2080592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:28.2080712Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:28.2080877Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:28.2081009Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:08:28.2081078Z 2025-03-04T21:08:28.2081409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:28.2081535Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:28.2081696Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:28.2081917Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:08:28.2081993Z 2025-03-04T21:08:28.2082320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:28.2082419Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:28.2082549Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:28.2082614Z 2025-03-04T21:08:28.2082936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:28.2083033Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:28.2083159Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:28.2083224Z 2025-03-04T21:08:28.2083541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:28.2083658Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:28.2083796Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:28.2083862Z 2025-03-04T21:08:28.2084178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:28.2084311Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:28.2084447Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:28.2084527Z 2025-03-04T21:08:28.2084888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:28.2085084Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:28.2085171Z 2025-03-04T21:08:28.2085508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:28.2085677Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:08:28.2085750Z 2025-03-04T21:08:28.2086132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:28.2086316Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:08:28.2086381Z 2025-03-04T21:08:28.2086867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:08:28.2087004Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:08:28.2087078Z 2025-03-04T21:08:28.2087374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:28.2087524Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:08:28.2087588Z 2025-03-04T21:08:28.2088033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:28.2088414Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:08:28.2088537Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:08:28.2088658Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:08:28.2088734Z 2025-03-04T21:08:28.2089200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:28.2089378Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:08:28.2089612Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:08:28.2089689Z 2025-03-04T21:08:28.2090145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:28.2090326Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:28.2090393Z 2025-03-04T21:08:28.2090697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:28.2090849Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:08:28.2091011Z 2025-03-04T21:08:28.2091397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:08:28.2091595Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:08:28.2091661Z 2025-03-04T21:08:28.2091997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:28.2092171Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:08:28.2092246Z 2025-03-04T21:08:28.2092620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:08:28.2092770Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:08:28.2092837Z 2025-03-04T21:08:28.2093321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:08:28.2093468Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:08:28.2093591Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:28.2093756Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:08:28.2093889Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:08:28.2093961Z 2025-03-04T21:08:28.2094329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:08:28.2094457Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:08:28.2094524Z 2025-03-04T21:08:28.2094533Z 2025-03-04T21:08:28.2094693Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:28.2146416Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", 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L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:08:28.2146894Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:08:28.2147248Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.2147682Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2148073Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2148472Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2148852Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2149245Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.2149664Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2150087Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2150495Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2150879Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2151238Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.2151642Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2152044Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2152437Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2152818Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2153163Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.2153575Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2153980Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2154370Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2154741Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2155100Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:28.2155556Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2155979Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2156575Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2156915Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2157226Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.2157571Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2157913Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2158317Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2158697Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2159026Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.2159436Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2159854Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2160247Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2160633Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2160962Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.2161371Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2161787Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2162175Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2162556Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2162890Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.2163316Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2163767Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2185679Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2186238Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2186569Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.2186940Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2187311Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2187651Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2187967Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2188588Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.2188945Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2189295Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2189618Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2189940Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2190238Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.2190584Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2190932Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2191255Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2191577Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2191963Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.2192319Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2192703Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2193083Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2193440Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2193735Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.2194089Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2194437Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2194773Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2195091Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2195407Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:28.2195772Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2196136Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2196486Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2196815Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2197114Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.2197461Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2197811Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2198141Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2198482Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2198773Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.2199148Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2199528Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2199872Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2200195Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2200485Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.2200840Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2201184Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2201521Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2201906Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2202212Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.2202563Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2202908Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2203241Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2203553Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2203846Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.2204198Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2204537Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2204853Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2205186Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2205494Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.2205849Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2206205Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2206525Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2206843Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2207129Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.2207483Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2207834Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2208170Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2208498Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2208792Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.2209142Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2209489Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2209823Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2210140Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2210439Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.2210848Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2211203Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2211551Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2211867Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2214739Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.2215141Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2215536Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2215880Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2216205Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2216539Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.2216883Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2217236Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2217561Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2217887Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2218180Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.2218531Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2218872Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2219200Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2219517Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2219833Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:28.2220195Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2220575Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2220919Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2221282Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2221646Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.2222009Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2222358Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2222679Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2222999Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2223287Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.2223637Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2223983Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2224305Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2224626Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2224915Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.2225267Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2225607Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2225937Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2226261Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2226550Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.2226901Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2227256Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2227602Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2227953Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2228256Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.2228627Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2228988Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2229319Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2229645Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2229943Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.2230295Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2230655Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2230979Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2231315Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2231603Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.2231964Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2232313Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2232641Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2232972Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2233260Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.2233630Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2233969Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2234314Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2234649Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2234965Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.2235334Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2235688Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2236022Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2236339Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2236636Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.2236994Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2237346Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2237674Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2237999Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2238301Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.2238658Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2238998Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2239318Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2239633Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2239915Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.2240276Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2240626Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2240964Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2241301Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2241585Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.2241942Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2242282Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2242611Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2242928Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2243225Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.2243573Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2243924Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2244254Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2244567Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2244861Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.2245207Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2245555Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2245877Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2246202Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2246511Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.2246864Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2247240Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2247565Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2247907Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2248194Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.2248543Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2248884Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2249213Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2249526Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2249824Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.2250176Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2250517Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2250843Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2251159Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2251471Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:28.2251831Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2252195Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2252537Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2252894Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2253192Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.2253549Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2253927Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2254269Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2254691Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2254991Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.2255346Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2255702Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2256052Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2256388Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2256679Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.2257039Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2257382Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2257717Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2258031Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2258349Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:28.2258697Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2259046Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2259370Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2259710Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2260005Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:28.2260384Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2260733Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2261073Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2261395Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2261685Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:28.2262038Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:28.2262377Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:28.2262707Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:28.2263031Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:28.2263386Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:08:28.2263719Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:08:28.2264032Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:08:28.2264414Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:08:28.2264777Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:08:28.2265139Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:08:28.2265486Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:08:28.2265560Z 2025-03-04T21:08:28.2265871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2266373Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2266467Z 2025-03-04T21:08:28.2266750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2268240Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2268341Z 2025-03-04T21:08:28.2268639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:08:28.2268793Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:08:28.2268860Z 2025-03-04T21:08:28.2269237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:08:28.2269484Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:08:28.2269558Z 2025-03-04T21:08:28.2269823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2270267Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2270335Z 2025-03-04T21:08:28.2270613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2272189Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2272264Z 2025-03-04T21:08:28.2272566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2272708Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:08:28.2272780Z 2025-03-04T21:08:28.2273054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2273491Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2273573Z 2025-03-04T21:08:28.2273865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2275428Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2275502Z 2025-03-04T21:08:28.2275800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2275944Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:08:28.2276016Z 2025-03-04T21:08:28.2276272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2276735Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2276801Z 2025-03-04T21:08:28.2277079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2278604Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2278679Z 2025-03-04T21:08:28.2278940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2279386Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:28.2279459Z 2025-03-04T21:08:28.2279747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2281374Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2281478Z 2025-03-04T21:08:28.2281761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2281924Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:08:28.2281990Z 2025-03-04T21:08:28.2282296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2282453Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:08:28.2282528Z 2025-03-04T21:08:28.2282791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2283273Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2283341Z 2025-03-04T21:08:28.2283637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2285183Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2285252Z 2025-03-04T21:08:28.2285571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2285719Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:08:28.2285792Z 2025-03-04T21:08:28.2286042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2286502Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2286568Z 2025-03-04T21:08:28.2286843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2288603Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2288711Z 2025-03-04T21:08:28.2289011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2289156Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:08:28.2289228Z 2025-03-04T21:08:28.2289481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2289927Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2289993Z 2025-03-04T21:08:28.2290270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2291804Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2291871Z 2025-03-04T21:08:28.2292163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2292326Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:08:28.2292401Z 2025-03-04T21:08:28.2292685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2292847Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:08:28.2292913Z 2025-03-04T21:08:28.2293172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2293646Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2293741Z 2025-03-04T21:08:28.2294006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2296875Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2297114Z 2025-03-04T21:08:28.2297490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2297661Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:08:28.2297736Z 2025-03-04T21:08:28.2298032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2298504Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2298587Z 2025-03-04T21:08:28.2298875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2300500Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2300580Z 2025-03-04T21:08:28.2300890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2301052Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:08:28.2301121Z 2025-03-04T21:08:28.2301401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2301939Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2302020Z 2025-03-04T21:08:28.2302355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2303996Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2304095Z 2025-03-04T21:08:28.2304383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2304555Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:08:28.2304624Z 2025-03-04T21:08:28.2304921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2305082Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:08:28.2305150Z 2025-03-04T21:08:28.2305412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2305858Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2305932Z 2025-03-04T21:08:28.2306201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2307745Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2307817Z 2025-03-04T21:08:28.2308110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2308270Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:08:28.2308335Z 2025-03-04T21:08:28.2308599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2309062Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2309157Z 2025-03-04T21:08:28.2309451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2310982Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2311081Z 2025-03-04T21:08:28.2311375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2311531Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:08:28.2311599Z 2025-03-04T21:08:28.2311861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2312298Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2312371Z 2025-03-04T21:08:28.2312643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2314221Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2314303Z 2025-03-04T21:08:28.2314564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2315024Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:28.2315098Z 2025-03-04T21:08:28.2315398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2317032Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2317148Z 2025-03-04T21:08:28.2317448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2317606Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:08:28.2317678Z 2025-03-04T21:08:28.2317965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2318128Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:08:28.2318198Z 2025-03-04T21:08:28.2318461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2318900Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2318974Z 2025-03-04T21:08:28.2319245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2320821Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2320900Z 2025-03-04T21:08:28.2321191Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2321352Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:08:28.2321416Z 2025-03-04T21:08:28.2321684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2322159Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2322234Z 2025-03-04T21:08:28.2322507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2324110Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2324207Z 2025-03-04T21:08:28.2324494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2324645Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:08:28.2324718Z 2025-03-04T21:08:28.2324981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2325421Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2325494Z 2025-03-04T21:08:28.2325761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2329338Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2329577Z 2025-03-04T21:08:28.2329936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2330131Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:08:28.2330201Z 2025-03-04T21:08:28.2330575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2330754Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:08:28.2330831Z 2025-03-04T21:08:28.2331141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2331778Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2331889Z 2025-03-04T21:08:28.2332178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2333842Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2333954Z 2025-03-04T21:08:28.2334267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2334426Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:08:28.2334500Z 2025-03-04T21:08:28.2334950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2335431Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2335502Z 2025-03-04T21:08:28.2336558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2338182Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2338260Z 2025-03-04T21:08:28.2338574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2338728Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:08:28.2338807Z 2025-03-04T21:08:28.2339072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2339570Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2339639Z 2025-03-04T21:08:28.2339927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2342186Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2342302Z 2025-03-04T21:08:28.2342685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2342865Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:08:28.2343771Z 2025-03-04T21:08:28.2344098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2344274Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:08:28.2344343Z 2025-03-04T21:08:28.2344626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2345125Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2345203Z 2025-03-04T21:08:28.2345636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2347340Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2347434Z 2025-03-04T21:08:28.2347731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2347892Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:08:28.2347959Z 2025-03-04T21:08:28.2348221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2348702Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2348799Z 2025-03-04T21:08:28.2349075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2350630Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2350731Z 2025-03-04T21:08:28.2351031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2351188Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:08:28.2351254Z 2025-03-04T21:08:28.2351518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2351965Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2352030Z 2025-03-04T21:08:28.2352303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2353862Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2353939Z 2025-03-04T21:08:28.2354232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2354397Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:08:28.2354464Z 2025-03-04T21:08:28.2354758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2354918Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:08:28.2355018Z 2025-03-04T21:08:28.2355278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2355733Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2355822Z 2025-03-04T21:08:28.2356092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2357644Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2357716Z 2025-03-04T21:08:28.2358010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2358158Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:08:28.2358223Z 2025-03-04T21:08:28.2358484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2358920Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2358996Z 2025-03-04T21:08:28.2359273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2360808Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2360888Z 2025-03-04T21:08:28.2361571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2361740Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:08:28.2361845Z 2025-03-04T21:08:28.2362168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2362604Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2362701Z 2025-03-04T21:08:28.2362991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2364534Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2364633Z 2025-03-04T21:08:28.2364889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2365332Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:28.2365398Z 2025-03-04T21:08:28.2365669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2367282Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2367363Z 2025-03-04T21:08:28.2367653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2367804Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:08:28.2367875Z 2025-03-04T21:08:28.2368162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2368312Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:08:28.2368380Z 2025-03-04T21:08:28.2368640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2369089Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2369181Z 2025-03-04T21:08:28.2369451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2371024Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2371125Z 2025-03-04T21:08:28.2371423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2371569Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:08:28.2371636Z 2025-03-04T21:08:28.2371900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2372331Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2372408Z 2025-03-04T21:08:28.2372674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2374275Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2374361Z 2025-03-04T21:08:28.2374782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2374941Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:08:28.2375006Z 2025-03-04T21:08:28.2375267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2375769Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2375846Z 2025-03-04T21:08:28.2376128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2377740Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2377849Z 2025-03-04T21:08:28.2378131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2378294Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:08:28.2378360Z 2025-03-04T21:08:28.2378655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2378803Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:08:28.2378876Z 2025-03-04T21:08:28.2379131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2379563Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2379630Z 2025-03-04T21:08:28.2379909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2381455Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2381527Z 2025-03-04T21:08:28.2381825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2381963Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:08:28.2382038Z 2025-03-04T21:08:28.2382291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2382751Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2382845Z 2025-03-04T21:08:28.2383118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2384670Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2384757Z 2025-03-04T21:08:28.2385060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2385198Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:08:28.2385274Z 2025-03-04T21:08:28.2385531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2385970Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2386043Z 2025-03-04T21:08:28.2386310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2387869Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2387940Z 2025-03-04T21:08:28.2388463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2388621Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:08:28.2388693Z 2025-03-04T21:08:28.2388974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2389126Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:08:28.2389200Z 2025-03-04T21:08:28.2389560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2389972Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2390070Z 2025-03-04T21:08:28.2390377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2391904Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2392021Z 2025-03-04T21:08:28.2392313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2392449Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:08:28.2392520Z 2025-03-04T21:08:28.2392772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2393203Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2393272Z 2025-03-04T21:08:28.2393552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2395062Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2395136Z 2025-03-04T21:08:28.2395429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2395577Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:08:28.2395647Z 2025-03-04T21:08:28.2395890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2396335Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2396417Z 2025-03-04T21:08:28.2396685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2398182Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2398273Z 2025-03-04T21:08:28.2398563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2398715Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:08:28.2398789Z 2025-03-04T21:08:28.2399068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2399216Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:08:28.2399284Z 2025-03-04T21:08:28.2399546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2399969Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2400043Z 2025-03-04T21:08:28.2400305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2401831Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2401907Z 2025-03-04T21:08:28.2402201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2402347Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:08:28.2402412Z 2025-03-04T21:08:28.2402696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2403124Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2403218Z 2025-03-04T21:08:28.2403513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2405034Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2405168Z 2025-03-04T21:08:28.2405461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2405603Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:08:28.2405667Z 2025-03-04T21:08:28.2405925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2406354Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2406426Z 2025-03-04T21:08:28.2406691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2408246Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2408325Z 2025-03-04T21:08:28.2408610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2408763Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:08:28.2408832Z 2025-03-04T21:08:28.2409129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2409294Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:08:28.2409368Z 2025-03-04T21:08:28.2409622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2410083Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2410152Z 2025-03-04T21:08:28.2410428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2411985Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2412058Z 2025-03-04T21:08:28.2412350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2412484Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:08:28.2412555Z 2025-03-04T21:08:28.2412809Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2413237Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2413309Z 2025-03-04T21:08:28.2413581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2415289Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2415373Z 2025-03-04T21:08:28.2415688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2415834Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:08:28.2416019Z 2025-03-04T21:08:28.2416354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2416793Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2416883Z 2025-03-04T21:08:28.2417218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2418782Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2418874Z 2025-03-04T21:08:28.2419175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2419321Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:08:28.2419395Z 2025-03-04T21:08:28.2419677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2419827Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:08:28.2419894Z 2025-03-04T21:08:28.2420158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2420571Z x_88: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2420647Z 2025-03-04T21:08:28.2420915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2422449Z x_89: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2422525Z 2025-03-04T21:08:28.2422824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2422994Z out_52: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:08:28.2423063Z 2025-03-04T21:08:28.2423326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2423796Z x_90: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_52 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2423870Z 2025-03-04T21:08:28.2424144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2425749Z x_91: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2425833Z 2025-03-04T21:08:28.2426129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2426271Z out_53: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:08:28.2426338Z 2025-03-04T21:08:28.2426606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2427047Z x_92: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2427127Z 2025-03-04T21:08:28.2427406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2428981Z x_93: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2429063Z 2025-03-04T21:08:28.2429323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2429812Z x_94: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_51 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:28.2429881Z 2025-03-04T21:08:28.2430164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2431862Z x_95: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2431949Z 2025-03-04T21:08:28.2432253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2432403Z x_93 += x_95; out_54: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_93; x_93 = x_95 = None 2025-03-04T21:08:28.2432482Z 2025-03-04T21:08:28.2432776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2432933Z out_55: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:08:28.2432999Z 2025-03-04T21:08:28.2433251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2433669Z x_96: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2433734Z 2025-03-04T21:08:28.2434003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2435495Z x_97: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2435571Z 2025-03-04T21:08:28.2435850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2435989Z out_56: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:08:28.2436055Z 2025-03-04T21:08:28.2436308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2436746Z x_98: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_56 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2436824Z 2025-03-04T21:08:28.2437093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2438592Z x_99: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2438682Z 2025-03-04T21:08:28.2438965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2439107Z out_57: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_99); x_99 = None 2025-03-04T21:08:28.2439180Z 2025-03-04T21:08:28.2439428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2439859Z x_100: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2439924Z 2025-03-04T21:08:28.2440195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2441710Z x_101: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2441790Z 2025-03-04T21:08:28.2442079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2442232Z x_101 += out_55; out_58: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_101; x_101 = out_55 = None 2025-03-04T21:08:28.2442302Z 2025-03-04T21:08:28.2442583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2442732Z out_59: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:08:28.2442795Z 2025-03-04T21:08:28.2443072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2443485Z x_102: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:28.2443573Z 2025-03-04T21:08:28.2443865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2445365Z x_103: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2445439Z 2025-03-04T21:08:28.2445723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2445875Z out_60: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:08:28.2445940Z 2025-03-04T21:08:28.2446200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2446619Z x_104: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_60 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:28.2446690Z 2025-03-04T21:08:28.2446964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2448470Z x_105: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2448545Z 2025-03-04T21:08:28.2448831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2448977Z out_61: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_105); x_105 = None 2025-03-04T21:08:28.2449042Z 2025-03-04T21:08:28.2449298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2450155Z x_106: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:28.2450248Z 2025-03-04T21:08:28.2450518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:28.2452058Z x_107: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:28.2452149Z 2025-03-04T21:08:28.2452432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:28.2452599Z x_107 += out_59; out_62: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_107; x_107 = out_59 = None 2025-03-04T21:08:28.2452665Z 2025-03-04T21:08:28.2452959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:28.2453107Z out_63: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:08:28.2453178Z 2025-03-04T21:08:28.2453613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:28.2453777Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:08:28.2453845Z 2025-03-04T21:08:28.2454150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:28.2454292Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:08:28.2454369Z 2025-03-04T21:08:28.2454961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:28.2455138Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:08:28.2455210Z 2025-03-04T21:08:28.2455535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:28.2455683Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:08:28.2455765Z 2025-03-04T21:08:28.2456177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:28.2456372Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:08:28.2456473Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:08:28.2456649Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:08:28.2456718Z 2025-03-04T21:08:28.2457066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:28.2457210Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:08:28.2457283Z 2025-03-04T21:08:28.2457628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:28.2457798Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:08:28.2457866Z 2025-03-04T21:08:28.2458257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:28.2458483Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:08:28.2458548Z 2025-03-04T21:08:28.2458975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:28.2459106Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:08:28.2459540Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:08:28.2459668Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:08:28.2459795Z x_108: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:08:28.2459859Z 2025-03-04T21:08:28.2460169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:28.2460296Z tensor: "f32[82125, 4][4, 1]cpu" = x_108.to(torch.float32); x_108 = None 2025-03-04T21:08:28.2460368Z 2025-03-04T21:08:28.2460623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:28.2461418Z x_109: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_63 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:08:28.2461488Z 2025-03-04T21:08:28.2461777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:28.2461973Z x_110: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_109, inplace = False); x_109 = None 2025-03-04T21:08:28.2462050Z 2025-03-04T21:08:28.2462436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:28.2463336Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:08:28.2463433Z 2025-03-04T21:08:28.2463803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:28.2464657Z x_111: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_110 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:08:28.2464739Z 2025-03-04T21:08:28.2465092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:08:28.2465247Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:08:28.2465400Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:08:28.2465466Z 2025-03-04T21:08:28.2465900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:08:28.2466069Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_111.view(4, -1, 4, 73, 75); x_111 = None 2025-03-04T21:08:28.2466245Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:08:28.2466432Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:08:28.2466496Z 2025-03-04T21:08:28.2466906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:28.2467118Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:08:28.2467190Z 2025-03-04T21:08:28.2467622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:28.2467780Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:08:28.2467931Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:08:28.2468075Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:08:28.2468140Z 2025-03-04T21:08:28.2468533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:28.2468701Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:08:28.2468771Z 2025-03-04T21:08:28.2469080Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:28.2469229Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:08:28.2469294Z 2025-03-04T21:08:28.2469634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:28.2469768Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:28.2469925Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:28.2470067Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:08:28.2470139Z 2025-03-04T21:08:28.2470470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:28.2470616Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:28.2470735Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:28.2470888Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:08:28.2470955Z 2025-03-04T21:08:28.2471273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:28.2471395Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:28.2471494Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:08:28.2471616Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:08:28.2471691Z 2025-03-04T21:08:28.2472003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:28.2472156Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:28.2472250Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:08:28.2472514Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:08:28.2472585Z 2025-03-04T21:08:28.2472948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:28.2473107Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:28.2473232Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:08:28.2473300Z 2025-03-04T21:08:28.2473614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:28.2473780Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:28.2473896Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:08:28.2473970Z 2025-03-04T21:08:28.2474272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:28.2474436Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:28.2474551Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:08:28.2474624Z 2025-03-04T21:08:28.2474928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:28.2475123Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:28.2475236Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:08:28.2475309Z 2025-03-04T21:08:28.2475677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:28.2475828Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:28.2475910Z 2025-03-04T21:08:28.2476251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:28.2476412Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:28.2476486Z 2025-03-04T21:08:28.2476863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:28.2477010Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:28.2477138Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:08:28.2477301Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:28.2477444Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:08:28.2477518Z 2025-03-04T21:08:28.2477869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:28.2478030Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:28.2478155Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:08:28.2478316Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:28.2478453Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:08:28.2478526Z 2025-03-04T21:08:28.2478856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:28.2478983Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:28.2479142Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:28.2479282Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:08:28.2479347Z 2025-03-04T21:08:28.2479695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:28.2479813Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:28.2479987Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:28.2480129Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:08:28.2480196Z 2025-03-04T21:08:28.2480519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:28.2480615Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:28.2480742Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:28.2480807Z 2025-03-04T21:08:28.2481129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:28.2481225Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:28.2481344Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:28.2481436Z 2025-03-04T21:08:28.2481753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:28.2481885Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:28.2482016Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:28.2482079Z 2025-03-04T21:08:28.2482406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:28.2482536Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:28.2482671Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:28.2482736Z 2025-03-04T21:08:28.2483094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:28.2483279Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:28.2483356Z 2025-03-04T21:08:28.2483693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:28.2483860Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:08:28.2483926Z 2025-03-04T21:08:28.2484319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:28.2484491Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:08:28.2484564Z 2025-03-04T21:08:28.2485048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:08:28.2485192Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:08:28.2485258Z 2025-03-04T21:08:28.2485567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:28.2485709Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:08:28.2485781Z 2025-03-04T21:08:28.2486220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:28.2486343Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:08:28.2486449Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:08:28.2486574Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:08:28.2486637Z 2025-03-04T21:08:28.2487115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:28.2487279Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:08:28.2487521Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:08:28.2487587Z 2025-03-04T21:08:28.2488233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:28.2488575Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:28.2488657Z 2025-03-04T21:08:28.2489029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:28.2489190Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:08:28.2489294Z 2025-03-04T21:08:28.2489674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:08:28.2489832Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:08:28.2489899Z 2025-03-04T21:08:28.2490205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:28.2490351Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:08:28.2490423Z 2025-03-04T21:08:28.2490799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:08:28.2490951Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:08:28.2491019Z 2025-03-04T21:08:28.2491521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:08:28.2491675Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:08:28.2491802Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:28.2491958Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:08:28.2492099Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:08:28.2492166Z 2025-03-04T21:08:28.2492541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:08:28.2492660Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:08:28.2492731Z 2025-03-04T21:08:38.0525304Z 2025-03-04T21:08:38.0530910Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:38.0534385Z def forward(self, L_features_res5_: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:08:38.0536131Z l_features_res5_ = L_features_res5_ 2025-03-04T21:08:38.0536561Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:08:38.0537412Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:08:38.0537908Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:08:38.0538523Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:08:38.0539173Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:08:38.0539743Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:08:38.0540348Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:08:38.0540716Z 2025-03-04T21:08:38.0541305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:38.0541971Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:08:38.0542247Z 2025-03-04T21:08:38.0542648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:38.0543145Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:08:38.0543409Z 2025-03-04T21:08:38.0543945Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:38.0544586Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:08:38.0544861Z 2025-03-04T21:08:38.0545248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:38.0545791Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:08:38.0546057Z 2025-03-04T21:08:38.0546549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:38.0547172Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:08:38.0547521Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:08:38.0547802Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:08:38.0548051Z 2025-03-04T21:08:38.0548488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:38.0549020Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:08:38.0549274Z 2025-03-04T21:08:38.0549700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:38.0550226Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:08:38.0550471Z 2025-03-04T21:08:38.0550942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:38.0551623Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:08:38.0551956Z 2025-03-04T21:08:38.0552462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:38.0553082Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:08:38.0553607Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:08:38.0554114Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:08:38.0554407Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:08:38.0554638Z 2025-03-04T21:08:38.0555034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:38.0555507Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:08:38.0555745Z 2025-03-04T21:08:38.0556090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:38.0557016Z x_1: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(l_features_res5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res5_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:08:38.0557733Z 2025-03-04T21:08:38.0558101Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:38.0558623Z x_2: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:08:38.0558924Z 2025-03-04T21:08:38.0559399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:38.0560479Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:08:38.0561230Z 2025-03-04T21:08:38.0561686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:38.0562668Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:08:38.0563357Z 2025-03-04T21:08:38.0563778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:08:38.0564319Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:08:38.0564660Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:08:38.0564917Z 2025-03-04T21:08:38.0565455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:08:38.0566092Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T21:08:38.0566469Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:08:38.0566875Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:08:38.0567182Z 2025-03-04T21:08:38.0567665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:38.0568316Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:08:38.0568631Z 2025-03-04T21:08:38.0569137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:38.0569757Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:08:38.0570107Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:08:38.0570440Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:08:38.0570698Z 2025-03-04T21:08:38.0571157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:38.0571757Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:08:38.0572050Z 2025-03-04T21:08:38.0572456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:38.0572968Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:08:38.0573231Z 2025-03-04T21:08:38.0573635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:38.0574140Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:38.0574550Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.0574905Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:08:38.0575185Z 2025-03-04T21:08:38.0575612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:38.0576130Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:38.0576452Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:38.0576779Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:08:38.0577051Z 2025-03-04T21:08:38.0577448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:38.0577949Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.0578225Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:08:38.0578491Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:08:38.0578754Z 2025-03-04T21:08:38.0579317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:38.0580136Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:38.0580435Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:08:38.0580749Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:08:38.0581004Z 2025-03-04T21:08:38.0581583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:38.0582396Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:38.0582896Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:08:38.0583151Z 2025-03-04T21:08:38.0583564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:38.0584097Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:38.0584431Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:08:38.0584669Z 2025-03-04T21:08:38.0585064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:38.0585575Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:38.0585902Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:08:38.0586141Z 2025-03-04T21:08:38.0586541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:38.0587108Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:38.0587479Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:08:38.0587721Z 2025-03-04T21:08:38.0588334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:38.0588902Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:38.0589171Z 2025-03-04T21:08:38.0589602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:38.0590136Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:38.0590405Z 2025-03-04T21:08:38.0590859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:38.0591415Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:38.0591745Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:08:38.0592085Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:38.0592442Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:08:38.0592705Z 2025-03-04T21:08:38.0593204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:38.0593757Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:38.0594080Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:08:38.0594512Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:38.0594862Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:08:38.0595152Z 2025-03-04T21:08:38.0595591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:38.0596145Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:38.0596477Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:38.0596830Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:08:38.0597089Z 2025-03-04T21:08:38.0597503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:38.0598012Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:38.0598352Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:38.0598706Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:08:38.0598967Z 2025-03-04T21:08:38.0599375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:38.0599837Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:38.0600109Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:38.0600348Z 2025-03-04T21:08:38.0600752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:38.0601224Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:38.0601479Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:38.0601709Z 2025-03-04T21:08:38.0602093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:38.0602558Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:38.0602848Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:38.0603099Z 2025-03-04T21:08:38.0603484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:38.0603947Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:38.0604238Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:38.0604488Z 2025-03-04T21:08:38.0604916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:38.0605485Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:38.0605776Z 2025-03-04T21:08:38.0606190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:38.0606754Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:08:38.0607039Z 2025-03-04T21:08:38.0607508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:38.0608115Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:08:38.0608417Z 2025-03-04T21:08:38.0608983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:08:38.0610406Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:08:38.0610656Z 2025-03-04T21:08:38.0611038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:38.0611521Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:08:38.0611784Z 2025-03-04T21:08:38.0612297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:38.0612889Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:08:38.0613161Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:08:38.0613430Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:08:38.0613656Z 2025-03-04T21:08:38.0614205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:38.0615011Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:08:38.0615490Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:08:38.0615857Z 2025-03-04T21:08:38.0616421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:38.0617110Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:38.0617402Z 2025-03-04T21:08:38.0617793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:38.0618301Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:08:38.0618574Z 2025-03-04T21:08:38.0619041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:08:38.0619623Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:08:38.0619896Z 2025-03-04T21:08:38.0620282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:38.0620785Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:08:38.0621051Z 2025-03-04T21:08:38.0621542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:08:38.0622124Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:08:38.0622411Z 2025-03-04T21:08:38.0623005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:08:38.0623696Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:08:38.0624038Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:38.0624374Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:08:38.0624719Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:08:38.0624974Z 2025-03-04T21:08:38.0625437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:08:38.0625984Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:08:38.0626223Z 2025-03-04T21:08:38.0626365Z 2025-03-04T21:08:38.0626464Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:38.0627815Z def forward(self, L_features_res5_: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:08:38.0629132Z l_features_res5_ = L_features_res5_ 2025-03-04T21:08:38.0629534Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:08:38.0630060Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:08:38.0630546Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:08:38.0631084Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:08:38.0631674Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:08:38.0632239Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:08:38.0632803Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:08:38.0633163Z 2025-03-04T21:08:38.0633697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:38.0634325Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:08:38.0634589Z 2025-03-04T21:08:38.0634984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:38.0635464Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:08:38.0635715Z 2025-03-04T21:08:38.0636261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:38.0636903Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:08:38.0637173Z 2025-03-04T21:08:38.0637562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:38.0638053Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:08:38.0638306Z 2025-03-04T21:08:38.0638771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:38.0639373Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:08:38.0639703Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:08:38.0639971Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:08:38.0640204Z 2025-03-04T21:08:38.0640615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:38.0641123Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:08:38.0641366Z 2025-03-04T21:08:38.0641774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:38.0642278Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:08:38.0642518Z 2025-03-04T21:08:38.0642989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:38.0643633Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:08:38.0643954Z 2025-03-04T21:08:38.0644449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:38.0645038Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:08:38.0645533Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:08:38.0646015Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:08:38.0646298Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:08:38.0646526Z 2025-03-04T21:08:38.0646915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:38.0647376Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:08:38.0647609Z 2025-03-04T21:08:38.0647949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:38.0648873Z x_1: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(l_features_res5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res5_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:08:38.0649604Z 2025-03-04T21:08:38.0649975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:38.0650522Z x_2: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:08:38.0650841Z 2025-03-04T21:08:38.0651296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:38.0652365Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:08:38.0653118Z 2025-03-04T21:08:38.0653572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:38.0654669Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:08:38.0655401Z 2025-03-04T21:08:38.0655836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:08:38.0656391Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:08:38.0656744Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:08:38.0657012Z 2025-03-04T21:08:38.0657530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:08:38.0658163Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T21:08:38.0658548Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:08:38.0658949Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:08:38.0659246Z 2025-03-04T21:08:38.0659745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:38.0660403Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:08:38.0660725Z 2025-03-04T21:08:38.0661251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:38.0661893Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:08:38.0662273Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:08:38.0662617Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:08:38.0662897Z 2025-03-04T21:08:38.0663365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:38.0663978Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:08:38.0664273Z 2025-03-04T21:08:38.0664682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:38.0665212Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:08:38.0665476Z 2025-03-04T21:08:38.0665878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:38.0666383Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:38.0666692Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.0667014Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:08:38.0667279Z 2025-03-04T21:08:38.0667688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:38.0668194Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:38.0668496Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:38.0668824Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:08:38.0669096Z 2025-03-04T21:08:38.0669502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:38.0669987Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.0670252Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:08:38.0670510Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:08:38.0670749Z 2025-03-04T21:08:38.0671140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:38.0671642Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:38.0671930Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:08:38.0672201Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:08:38.0672446Z 2025-03-04T21:08:38.0672842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:38.0673346Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:38.0673676Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:08:38.0673913Z 2025-03-04T21:08:38.0674293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:38.0674787Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:38.0675108Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:08:38.0675341Z 2025-03-04T21:08:38.0675736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:38.0676250Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:38.0676570Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:08:38.0676801Z 2025-03-04T21:08:38.0677196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:38.0677735Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:38.0678076Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:08:38.0678306Z 2025-03-04T21:08:38.0678720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:38.0679235Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:38.0679493Z 2025-03-04T21:08:38.0679906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:38.0680421Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:38.0680672Z 2025-03-04T21:08:38.0681101Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:38.0681628Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:38.0681949Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:08:38.0682278Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:38.0682621Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:08:38.0682874Z 2025-03-04T21:08:38.0683297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:38.0683826Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:38.0684130Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:08:38.0684455Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:38.0684796Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:08:38.0685052Z 2025-03-04T21:08:38.0685464Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:38.0685961Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:38.0686287Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:38.0686628Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:08:38.0686878Z 2025-03-04T21:08:38.0687292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:38.0687788Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:38.0688353Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:38.0688714Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:08:38.0688990Z 2025-03-04T21:08:38.0689387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:38.0689843Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:38.0690130Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:38.0690365Z 2025-03-04T21:08:38.0690782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:38.0691230Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:38.0691489Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:38.0691721Z 2025-03-04T21:08:38.0692108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:38.0692582Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:38.0692872Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:38.0693120Z 2025-03-04T21:08:38.0693515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:38.0693984Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:38.0694272Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:38.0694567Z 2025-03-04T21:08:38.0695003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:38.0695587Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:38.0695886Z 2025-03-04T21:08:38.0696308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:38.0696868Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:08:38.0697146Z 2025-03-04T21:08:38.0697617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:38.0698225Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:08:38.0698526Z 2025-03-04T21:08:38.0699100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:08:38.0699772Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:08:38.0700025Z 2025-03-04T21:08:38.0700414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:38.0700908Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:08:38.0701163Z 2025-03-04T21:08:38.0701709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:38.0702311Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:08:38.0702584Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:08:38.0702874Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:08:38.0703109Z 2025-03-04T21:08:38.0703679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:38.0704389Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:08:38.0704846Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:08:38.0705203Z 2025-03-04T21:08:38.0705757Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:38.0706447Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:38.0706738Z 2025-03-04T21:08:38.0707131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:38.0707647Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:08:38.0707924Z 2025-03-04T21:08:38.0708400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:08:38.0709003Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:08:38.0709299Z 2025-03-04T21:08:38.0709692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:38.0710199Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:08:38.0710459Z 2025-03-04T21:08:38.0710918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:08:38.0711483Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:08:38.0711742Z 2025-03-04T21:08:38.0712306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:08:38.0712965Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:08:38.0713277Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:38.0713601Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:08:38.0713935Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:08:38.0714183Z 2025-03-04T21:08:38.0714627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:08:38.0715158Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:08:38.0715389Z 2025-03-04T21:08:39.1265934Z 2025-03-04T21:08:39.1268211Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:39.1269333Z def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"): 2025-03-04T21:08:39.1270650Z l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_ 2025-03-04T21:08:39.1271031Z l_anchors_0_tensor = L_anchors_0_tensor 2025-03-04T21:08:39.1274178Z l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_ 2025-03-04T21:08:39.1274616Z 2025-03-04T21:08:39.1275440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:39.1276277Z pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4); l_pred_anchor_deltas_0_ = None 2025-03-04T21:08:39.1276620Z 2025-03-04T21:08:39.1277162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:39.1277856Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0); l_anchors_0_tensor = None 2025-03-04T21:08:39.1278248Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:08:39.1278602Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:08:39.1278867Z 2025-03-04T21:08:39.1279342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:39.1279942Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float(); pred_anchor_deltas_i = None 2025-03-04T21:08:39.1280233Z 2025-03-04T21:08:39.1280640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:39.1281161Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:08:39.1281429Z 2025-03-04T21:08:39.1281837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:39.1282348Z getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:39.1282702Z getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:39.1283061Z widths: "f32[328500][1]cpu" = getitem - getitem_1; getitem = getitem_1 = None 2025-03-04T21:08:39.1283387Z 2025-03-04T21:08:39.1283846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:39.1284418Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:39.1284731Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:39.1285072Z heights: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:08:39.1285349Z 2025-03-04T21:08:39.1285766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:39.1286323Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:39.1286599Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:08:39.1286874Z ctr_x: "f32[328500][1]cpu" = getitem_4 + mul; getitem_4 = mul = None 2025-03-04T21:08:39.1287125Z 2025-03-04T21:08:39.1287577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:39.1288223Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:39.1288704Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:08:39.1288978Z ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1; getitem_5 = mul_1 = None 2025-03-04T21:08:39.1289225Z 2025-03-04T21:08:39.1289681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:39.1290222Z getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:39.1290547Z dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0; getitem_6 = None 2025-03-04T21:08:39.1290780Z 2025-03-04T21:08:39.1291167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:39.1291668Z getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:39.1291991Z dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0; getitem_7 = None 2025-03-04T21:08:39.1292220Z 2025-03-04T21:08:39.1292601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:39.1293094Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:39.1293409Z dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:08:39.1293636Z 2025-03-04T21:08:39.1294026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:39.1294651Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:39.1295015Z dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:08:39.1295254Z 2025-03-04T21:08:39.1295694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:39.1296242Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:39.1296512Z 2025-03-04T21:08:39.1296938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:39.1297460Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:39.1297720Z 2025-03-04T21:08:39.1298148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:39.1298694Z getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:39.1299019Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10; dx = getitem_10 = None 2025-03-04T21:08:39.1299363Z getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:39.1299710Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11; mul_2 = getitem_11 = None 2025-03-04T21:08:39.1299967Z 2025-03-04T21:08:39.1300394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:39.1300924Z getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:39.1301270Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12; dy = getitem_12 = None 2025-03-04T21:08:39.1301597Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:39.1301977Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13; mul_3 = getitem_13 = None 2025-03-04T21:08:39.1302233Z 2025-03-04T21:08:39.1302664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:39.1303174Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:39.1303496Z getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:39.1303835Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14; exp = getitem_14 = None 2025-03-04T21:08:39.1304083Z 2025-03-04T21:08:39.1304499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:39.1304993Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:39.1305323Z getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:39.1305673Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15; exp_1 = getitem_15 = None 2025-03-04T21:08:39.1305925Z 2025-03-04T21:08:39.1306319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:39.1306780Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:39.1307043Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:39.1307276Z 2025-03-04T21:08:39.1307667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:39.1308117Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:39.1308375Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:39.1308605Z 2025-03-04T21:08:39.1308989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:39.1309460Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:39.1309749Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:39.1309992Z 2025-03-04T21:08:39.1310372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:39.1310835Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:39.1311121Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:39.1311367Z 2025-03-04T21:08:39.1311790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:39.1312363Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:39.1312655Z 2025-03-04T21:08:39.1313069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:39.1313606Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:08:39.1313877Z 2025-03-04T21:08:39.1314377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:39.1314992Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:08:39.1315284Z 2025-03-04T21:08:39.1315853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:08:39.1316556Z arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:08:39.1316803Z 2025-03-04T21:08:39.1317188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:39.1317670Z batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:08:39.1317920Z 2025-03-04T21:08:39.1318436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:39.1319090Z topk = l_pred_objectness_logits_0_.topk(6000, dim = 1); l_pred_objectness_logits_0_ = None 2025-03-04T21:08:39.1319424Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:08:39.1319691Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:08:39.1319926Z 2025-03-04T21:08:39.1320471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:39.1321139Z getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:08:39.1321590Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)]; proposals_i_1 = getitem_18 = topk_idx = None 2025-03-04T21:08:39.1321937Z 2025-03-04T21:08:39.1322472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:39.1323136Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:39.1323422Z 2025-03-04T21:08:39.1323798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:39.1324294Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:08:39.1324567Z 2025-03-04T21:08:39.1325028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:08:39.1325606Z getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:08:39.1325870Z 2025-03-04T21:08:39.1326247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:39.1326731Z tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32); getitem_20 = None 2025-03-04T21:08:39.1326988Z 2025-03-04T21:08:39.1327460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:08:39.1328019Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:08:39.1328311Z 2025-03-04T21:08:39.1328888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:08:39.1329562Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor); tensor = None 2025-03-04T21:08:39.1329870Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:39.1330218Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:08:39.1330559Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:08:39.1330812Z 2025-03-04T21:08:39.1331263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:08:39.1331796Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:08:39.1332029Z 2025-03-04T21:08:46.6783747Z 2025-03-04T21:08:46.6788939Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:46.6792594Z def forward(self, L_stack0_: "f32[3230, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"): 2025-03-04T21:08:46.6795031Z l_stack0_ = L_stack0_ 2025-03-04T21:08:46.6795388Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:08:46.6795862Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:08:46.6796324Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:08:46.6796781Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:08:46.6797283Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:08:46.6797836Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:08:46.6798384Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:08:46.6798930Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:08:46.6799396Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:08:46.6799792Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:08:46.6800490Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:08:46.6800890Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:08:46.6801245Z 2025-03-04T21:08:46.6801638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:08:46.6802163Z x: "f32[3230, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:08:46.6802862Z x_1: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:08:46.6803621Z x_2: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:08:46.6804329Z x_3: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:08:46.6805028Z x_4: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:08:46.6805302Z 2025-03-04T21:08:46.6805704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:08:46.6806660Z scores: "f32[3230, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:08:46.6807362Z 2025-03-04T21:08:46.6807773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:08:46.6808770Z proposal_deltas: "f32[3230, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:08:46.6809491Z 2025-03-04T21:08:46.6809924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:46.6810398Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:46.6810651Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:08:46.6810888Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:08:46.6811161Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:46.6811425Z getitem_2: "Sym(1230 - s0)" = size_1[0] 2025-03-04T21:08:46.6811667Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:08:46.6811890Z 2025-03-04T21:08:46.6812260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:46.6813181Z proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:08:46.6813880Z 2025-03-04T21:08:46.6814455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:46.6815107Z deltas: "f32[3230, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:08:46.6815399Z 2025-03-04T21:08:46.6815810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:46.6816378Z boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:08:46.6817634Z 2025-03-04T21:08:46.6818074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:46.6818613Z getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:46.6818944Z getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:46.6819287Z widths: "f32[3230][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:08:46.6819568Z 2025-03-04T21:08:46.6820010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:46.6820538Z getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:46.6820854Z getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:46.6821191Z heights: "f32[3230][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:08:46.6821469Z 2025-03-04T21:08:46.6821900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:46.6822425Z getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:46.6822701Z mul: "f32[3230][1]cpu" = 0.5 * widths 2025-03-04T21:08:46.6822977Z ctr_x: "f32[3230][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:08:46.6823225Z 2025-03-04T21:08:46.6823653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:46.6824194Z getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:46.6824502Z mul_1: "f32[3230][1]cpu" = 0.5 * heights 2025-03-04T21:08:46.6824764Z ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:08:46.6825002Z 2025-03-04T21:08:46.6825427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:46.6825935Z getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:46.6826260Z dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:08:46.6826493Z 2025-03-04T21:08:46.6826883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:46.6827402Z getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:46.6827725Z dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:08:46.6827963Z 2025-03-04T21:08:46.6828363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:46.6828898Z getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:46.6829230Z dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:08:46.6829469Z 2025-03-04T21:08:46.6829883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:46.6830445Z getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:46.6830869Z dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:08:46.6831107Z 2025-03-04T21:08:46.6831551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:46.6832078Z dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:46.6832334Z 2025-03-04T21:08:46.6832748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:46.6833262Z dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:46.6833511Z 2025-03-04T21:08:46.6833939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:46.6834466Z getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:46.6834779Z mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:08:46.6835107Z getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:46.6835451Z pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:08:46.6835708Z 2025-03-04T21:08:46.6836135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:46.6836668Z getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:46.6836976Z mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:08:46.6837298Z getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:46.6837634Z pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:08:46.6837884Z 2025-03-04T21:08:46.6838292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:46.6838790Z exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:46.6839110Z getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:46.6839448Z pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:08:46.6839693Z 2025-03-04T21:08:46.6840106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:46.6840600Z exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:46.6840921Z getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:46.6841259Z pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:08:46.6841505Z 2025-03-04T21:08:46.6841908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:46.6842370Z mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:46.6842643Z x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:46.6842879Z 2025-03-04T21:08:46.6843293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:46.6843747Z mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:46.6844035Z y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:46.6844268Z 2025-03-04T21:08:46.6844660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:46.6845132Z mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:46.6845461Z x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:46.6845745Z 2025-03-04T21:08:46.6846135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:46.6846600Z mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:46.6846884Z y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:46.6847125Z 2025-03-04T21:08:46.6847556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:46.6848129Z pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:46.6848416Z 2025-03-04T21:08:46.6848832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:46.6849384Z predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320)); pred_boxes = None 2025-03-04T21:08:46.6849666Z 2025-03-04T21:08:46.6850103Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:08:46.6850709Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:08:46.6851073Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:08:46.6851356Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:08:46.6851656Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:08:46.6851966Z getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:08:46.6852224Z 2025-03-04T21:08:46.6852599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:46.6853160Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:08:46.6853515Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:08:46.6853766Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:08:46.6854138Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:08:46.6854579Z getitem_26: "Sym(1230 - s0)" = size_3[0] 2025-03-04T21:08:46.6854845Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:08:46.6855082Z 2025-03-04T21:08:46.6855558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:08:46.6856182Z probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:08:46.6856520Z 2025-03-04T21:08:46.6856973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:08:46.6857564Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:08:46.6857939Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:08:46.6858225Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:08:46.6858530Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:08:46.6858850Z getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:08:46.6859121Z 2025-03-04T21:08:46.6859672Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:46.6860383Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:08:46.6860732Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:46.6861076Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:08:46.6861426Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:46.6861733Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:46.6861971Z 2025-03-04T21:08:46.6862417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:46.6862953Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:46.6863185Z 2025-03-04T21:08:46.6882284Z 2025-03-04T21:08:46.6887244Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:46.6890896Z def forward(self, L_stack0_: "f32[3230, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"): 2025-03-04T21:08:46.6893088Z l_stack0_ = L_stack0_ 2025-03-04T21:08:46.6893459Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:08:46.6893966Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:08:46.6894796Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:08:46.6895355Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:08:46.6895941Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:08:46.6896566Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:08:46.6897152Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:08:46.6897769Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:08:46.6898271Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:08:46.6898694Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:08:46.6899084Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:08:46.6899471Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:08:46.6899762Z 2025-03-04T21:08:46.6900157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:08:46.6900634Z x: "f32[3230, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:08:46.6901321Z x_1: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:08:46.6902021Z x_2: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:08:46.6902721Z x_3: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:08:46.6903413Z x_4: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:08:46.6903687Z 2025-03-04T21:08:46.6904089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:08:46.6905030Z scores: "f32[3230, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:08:46.6905745Z 2025-03-04T21:08:46.6906172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:08:46.6907164Z proposal_deltas: "f32[3230, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:08:46.6908894Z 2025-03-04T21:08:46.6909592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:46.6910149Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:46.6910414Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:08:46.6910653Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:08:46.6910965Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:46.6911223Z getitem_2: "Sym(1230 - s0)" = size_1[0] 2025-03-04T21:08:46.6911469Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:08:46.6911751Z 2025-03-04T21:08:46.6912127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:46.6914018Z proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:08:46.6914753Z 2025-03-04T21:08:46.6915221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:46.6915802Z deltas: "f32[3230, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:08:46.6916079Z 2025-03-04T21:08:46.6916487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:46.6917017Z boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:08:46.6917301Z 2025-03-04T21:08:46.6917705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:46.6918216Z getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:46.6918513Z getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:46.6918827Z widths: "f32[3230][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:08:46.6919090Z 2025-03-04T21:08:46.6919516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:46.6919999Z getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:46.6920289Z getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:46.6920597Z heights: "f32[3230][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:08:46.6920857Z 2025-03-04T21:08:46.6921252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:46.6921729Z getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:46.6921982Z mul: "f32[3230][1]cpu" = 0.5 * widths 2025-03-04T21:08:46.6922235Z ctr_x: "f32[3230][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:08:46.6922470Z 2025-03-04T21:08:46.6922862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:46.6923360Z getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:46.6923641Z mul_1: "f32[3230][1]cpu" = 0.5 * heights 2025-03-04T21:08:46.6923904Z ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:08:46.6924133Z 2025-03-04T21:08:46.6924578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:46.6925088Z getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:46.6925431Z dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:08:46.6925665Z 2025-03-04T21:08:46.6926084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:46.6926593Z getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:46.6926930Z dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:08:46.6927159Z 2025-03-04T21:08:46.6927536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:46.6928028Z getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:46.6928346Z dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:08:46.6928578Z 2025-03-04T21:08:46.6928961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:46.6929486Z getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:46.6929826Z dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:08:46.6930052Z 2025-03-04T21:08:46.6930462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:46.6930980Z dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:46.6931231Z 2025-03-04T21:08:46.6931635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:46.6932145Z dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:46.6932391Z 2025-03-04T21:08:46.6932815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:46.6933345Z getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:46.6933651Z mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:08:46.6933978Z getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:46.6934323Z pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:08:46.6934685Z 2025-03-04T21:08:46.6935204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:46.6935839Z getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:46.6936261Z mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:08:46.6936645Z getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:46.6937044Z pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:08:46.6937345Z 2025-03-04T21:08:46.6937875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:46.6938472Z exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:46.6938875Z getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:46.6939274Z pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:08:46.6939566Z 2025-03-04T21:08:46.6940090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:46.6940701Z exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:46.6941083Z getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:46.6941483Z pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:08:46.6941776Z 2025-03-04T21:08:46.6942236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:46.6942726Z mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:46.6942985Z x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:46.6943224Z 2025-03-04T21:08:46.6943623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:46.6944080Z mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:46.6944333Z y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:46.6944568Z 2025-03-04T21:08:46.6944975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:46.6945439Z mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:46.6945726Z x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:46.6945967Z 2025-03-04T21:08:46.6946360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:46.6946835Z mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:46.6947819Z y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:46.6948117Z 2025-03-04T21:08:46.6948959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:46.6949577Z pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:46.6949877Z 2025-03-04T21:08:46.6950305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:46.6950875Z predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320)); pred_boxes = None 2025-03-04T21:08:46.6951171Z 2025-03-04T21:08:46.6951623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:08:46.6952243Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:08:46.6952609Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:08:46.6952950Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:08:46.6953264Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:08:46.6953607Z getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:08:46.6953878Z 2025-03-04T21:08:46.6954272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:46.6955183Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:08:46.6955576Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:08:46.6956182Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:08:46.6956551Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:08:46.6956916Z getitem_26: "Sym(1230 - s0)" = size_3[0] 2025-03-04T21:08:46.6957172Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:08:46.6957399Z 2025-03-04T21:08:46.6957844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:08:46.6958411Z probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:08:46.6958733Z 2025-03-04T21:08:46.6959179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:08:46.6960071Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:08:46.6960440Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:08:46.6960742Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:08:46.6961052Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:08:46.6961377Z getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:08:46.6961634Z 2025-03-04T21:08:46.6962194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:46.6963192Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:08:46.6963556Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:46.6963896Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:08:46.6964253Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:46.6964561Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:46.6964854Z 2025-03-04T21:08:46.6965334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:46.6965888Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:46.6966130Z 2025-03-04T21:08:46.6966271Z 2025-03-04T21:08:46.6966373Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:46.6968410Z def forward(self, L_stack0_: "f32[3230, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"): 2025-03-04T21:08:46.6970616Z l_stack0_ = L_stack0_ 2025-03-04T21:08:46.6970977Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:08:46.6971478Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:08:46.6971978Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:08:46.6972471Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:08:46.6972975Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:08:46.6973531Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:08:46.6974098Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:08:46.6974819Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:08:46.6975366Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:08:46.6975805Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:08:46.6976214Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:08:46.6976619Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:08:46.6976926Z 2025-03-04T21:08:46.6977309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:08:46.6977802Z x: "f32[3230, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:08:46.6978532Z x_1: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:08:46.6979259Z x_2: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:08:46.6979976Z x_3: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:08:46.6980703Z x_4: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:08:46.6980985Z 2025-03-04T21:08:46.6981400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:08:46.6982418Z scores: "f32[3230, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:08:46.6983153Z 2025-03-04T21:08:46.6983575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:08:46.6984544Z proposal_deltas: "f32[3230, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:08:46.6985271Z 2025-03-04T21:08:46.6985649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:46.6986114Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:46.6986375Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:08:46.6986612Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:08:46.6986890Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:46.6987718Z getitem_2: "Sym(1230 - s0)" = size_1[0] 2025-03-04T21:08:46.6987987Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:08:46.6988567Z 2025-03-04T21:08:46.6988952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:46.6989911Z proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:08:46.6990640Z 2025-03-04T21:08:46.6991109Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:46.6991691Z deltas: "f32[3230, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:08:46.6991970Z 2025-03-04T21:08:46.6992369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:46.6992895Z boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:08:46.6993176Z 2025-03-04T21:08:46.6993585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:46.6994085Z getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:46.6994399Z getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:46.6994721Z widths: "f32[3230][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:08:46.6994986Z 2025-03-04T21:08:46.6995398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:46.6995896Z getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:46.6996196Z getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:46.6996516Z heights: "f32[3230][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:08:46.6996885Z 2025-03-04T21:08:46.6997292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:46.6997819Z getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:46.6998139Z mul: "f32[3230][1]cpu" = 0.5 * widths 2025-03-04T21:08:46.6998416Z ctr_x: "f32[3230][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:08:46.6998695Z 2025-03-04T21:08:46.6999108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:46.6999657Z getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:46.6999948Z mul_1: "f32[3230][1]cpu" = 0.5 * heights 2025-03-04T21:08:46.7000212Z ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:08:46.7000458Z 2025-03-04T21:08:46.7000878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:46.7001400Z getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:46.7001731Z dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:08:46.7001973Z 2025-03-04T21:08:46.7002366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:46.7002883Z getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:46.7003211Z dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:08:46.7003449Z 2025-03-04T21:08:46.7003846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:46.7004340Z getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:46.7004656Z dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:08:46.7004885Z 2025-03-04T21:08:46.7005272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:46.7005799Z getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:46.7006139Z dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:08:46.7006368Z 2025-03-04T21:08:46.7006784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:46.7007311Z dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:46.7007562Z 2025-03-04T21:08:46.7007976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:46.7008489Z dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:46.7008736Z 2025-03-04T21:08:46.7009161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:46.7009692Z getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:46.7010023Z mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:08:46.7011226Z getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:46.7011619Z pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:08:46.7011885Z 2025-03-04T21:08:46.7012354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:46.7012900Z getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:46.7013246Z mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:08:46.7013578Z getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:46.7013921Z pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:08:46.7014186Z 2025-03-04T21:08:46.7014682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:46.7015216Z exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:46.7015557Z getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:46.7015918Z pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:08:46.7016196Z 2025-03-04T21:08:46.7016629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:46.7017195Z exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:46.7017575Z getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:46.7017919Z pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:08:46.7018172Z 2025-03-04T21:08:46.7018569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:46.7019026Z mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:46.7019288Z x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:46.7019520Z 2025-03-04T21:08:46.7019910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:46.7020357Z mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:46.7020616Z y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:46.7020848Z 2025-03-04T21:08:46.7021235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:46.7021703Z mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:46.7021987Z x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:46.7022230Z 2025-03-04T21:08:46.7022612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:46.7023072Z mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:46.7023349Z y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:46.7023589Z 2025-03-04T21:08:46.7024029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:46.7024619Z pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:46.7024932Z 2025-03-04T21:08:46.7025352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:46.7025922Z predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320)); pred_boxes = None 2025-03-04T21:08:46.7026224Z 2025-03-04T21:08:46.7026680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:08:46.7027297Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:08:46.7027658Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:08:46.7027947Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:08:46.7028251Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:08:46.7028569Z getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:08:46.7028828Z 2025-03-04T21:08:46.7029203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:46.7029749Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:08:46.7030092Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:08:46.7030335Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:08:46.7030694Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:08:46.7031042Z getitem_26: "Sym(1230 - s0)" = size_3[0] 2025-03-04T21:08:46.7031287Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:08:46.7031507Z 2025-03-04T21:08:46.7031922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:08:46.7032487Z probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:08:46.7032768Z 2025-03-04T21:08:46.7033198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:08:46.7033782Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:08:46.7034133Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:08:46.7034420Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:08:46.7034717Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:08:46.7035026Z getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:08:46.7035281Z 2025-03-04T21:08:46.7035816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:46.7036489Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:08:46.7036872Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:46.7037241Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:08:46.7037581Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:46.7037895Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:46.7038135Z 2025-03-04T21:08:46.7038590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:46.7039104Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:46.7039360Z 2025-03-04T21:08:46.7039453Z 2025-03-04T21:08:46.7039540Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:46.7041450Z def forward(self, L_stack0_: "f32[3230, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"): 2025-03-04T21:08:46.7043486Z l_stack0_ = L_stack0_ 2025-03-04T21:08:46.7043833Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:08:46.7044314Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:08:46.7044791Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:08:46.7045265Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:08:46.7045785Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:08:46.7046337Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:08:46.7046896Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:08:46.7047442Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:08:46.7047910Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:08:46.7048311Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:08:46.7048705Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:08:46.7049095Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:08:46.7049385Z 2025-03-04T21:08:46.7049750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:08:46.7050215Z x: "f32[3230, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:08:46.7050927Z x_1: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:08:46.7051645Z x_2: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:08:46.7052363Z x_3: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:08:46.7053072Z x_4: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:08:46.7053352Z 2025-03-04T21:08:46.7053748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:08:46.7054861Z scores: "f32[3230, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:08:46.7055690Z 2025-03-04T21:08:46.7056151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:08:46.7057164Z proposal_deltas: "f32[3230, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:08:46.7057895Z 2025-03-04T21:08:46.7058274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:46.7058742Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:46.7059002Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:08:46.7059239Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:08:46.7059518Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:46.7059782Z getitem_2: "Sym(1230 - s0)" = size_1[0] 2025-03-04T21:08:46.7060033Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:08:46.7060255Z 2025-03-04T21:08:46.7060629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:46.7061564Z proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:08:46.7062267Z 2025-03-04T21:08:46.7062734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:46.7063314Z deltas: "f32[3230, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:08:46.7063591Z 2025-03-04T21:08:46.7063989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:46.7064536Z boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:08:46.7064819Z 2025-03-04T21:08:46.7065222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:46.7065741Z getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:46.7066053Z getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:46.7066393Z widths: "f32[3230][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:08:46.7066661Z 2025-03-04T21:08:46.7067085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:46.7067588Z getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:46.7067889Z getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:46.7068209Z heights: "f32[3230][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:08:46.7068478Z 2025-03-04T21:08:46.7068889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:46.7069380Z getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:46.7069647Z mul: "f32[3230][1]cpu" = 0.5 * widths 2025-03-04T21:08:46.7069907Z ctr_x: "f32[3230][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:08:46.7070152Z 2025-03-04T21:08:46.7070561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:46.7071077Z getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:46.7071369Z mul_1: "f32[3230][1]cpu" = 0.5 * heights 2025-03-04T21:08:46.7071636Z ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:08:46.7071882Z 2025-03-04T21:08:46.7072295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:46.7072810Z getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:46.7073145Z dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:08:46.7073383Z 2025-03-04T21:08:46.7073775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:46.7074285Z getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:46.7074619Z dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:08:46.7074850Z 2025-03-04T21:08:46.7075237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:46.7075735Z getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:46.7076055Z dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:08:46.7076283Z 2025-03-04T21:08:46.7076669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:46.7077199Z getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:46.7077541Z dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:08:46.7077792Z 2025-03-04T21:08:46.7078215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:46.7078749Z dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:46.7079002Z 2025-03-04T21:08:46.7079423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:46.7079930Z dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:46.7080209Z 2025-03-04T21:08:46.7080631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:46.7081161Z getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:46.7081473Z mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:08:46.7081800Z getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:46.7082140Z pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:08:46.7082394Z 2025-03-04T21:08:46.7082825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:46.7083354Z getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:46.7083664Z mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:08:46.7083990Z getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:46.7084329Z pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:08:46.7084583Z 2025-03-04T21:08:46.7084992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:46.7085490Z exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:46.7085814Z getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:46.7086150Z pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:08:46.7086398Z 2025-03-04T21:08:46.7086812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:46.7087303Z exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:46.7087627Z getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:46.7087969Z pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:08:46.7088430Z 2025-03-04T21:08:46.7088836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:46.7089294Z mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:46.7089558Z x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:46.7089794Z 2025-03-04T21:08:46.7090185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:46.7090696Z mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:46.7090960Z y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:46.7091194Z 2025-03-04T21:08:46.7091610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:46.7092082Z mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:46.7092402Z x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:46.7092654Z 2025-03-04T21:08:46.7093039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:46.7093537Z mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:46.7093818Z y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:46.7094065Z 2025-03-04T21:08:46.7095508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:46.7096184Z pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:46.7096485Z 2025-03-04T21:08:46.7096906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:46.7097457Z predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320)); pred_boxes = None 2025-03-04T21:08:46.7097739Z 2025-03-04T21:08:46.7098184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:08:46.7098794Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:08:46.7099155Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:08:46.7099452Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:08:46.7099766Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:08:46.7100080Z getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:08:46.7100341Z 2025-03-04T21:08:46.7100717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:46.7101272Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:08:46.7101617Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:08:46.7101856Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:08:46.7102216Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:08:46.7102569Z getitem_26: "Sym(1230 - s0)" = size_3[0] 2025-03-04T21:08:46.7102815Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:08:46.7103030Z 2025-03-04T21:08:46.7103442Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:08:46.7103998Z probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:08:46.7104283Z 2025-03-04T21:08:46.7104715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:08:46.7105368Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:08:46.7105725Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:08:46.7106039Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:08:46.7106340Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:08:46.7106679Z getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:08:46.7106942Z 2025-03-04T21:08:46.7107776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:46.7108491Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:08:46.7108835Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:46.7109168Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:08:46.7109507Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:46.7109797Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:46.7110034Z 2025-03-04T21:08:46.7110469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:46.7110981Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:46.7111214Z 2025-03-04T21:08:48.8295523Z 2025-03-04T21:08:48.8296285Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:48.8297542Z def forward(self, L_predictions_0_: "f32[3230, 81][81, 1]cpu", L_predictions_1_: "f32[3230, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"): 2025-03-04T21:08:48.8298547Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:08:48.8298796Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:08:48.8299143Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:08:48.8299624Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:08:48.8300048Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:08:48.8300462Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:08:48.8300773Z 2025-03-04T21:08:48.8301220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:48.8301727Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:48.8301996Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:08:48.8302243Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:08:48.8302545Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:48.8302827Z getitem_2: "Sym(1230 - s0)" = size_1[0] 2025-03-04T21:08:48.8303079Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:08:48.8303316Z 2025-03-04T21:08:48.8303715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:48.8305026Z proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:08:48.8305864Z 2025-03-04T21:08:48.8306344Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:48.8306998Z deltas: "f32[3230, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:08:48.8307357Z 2025-03-04T21:08:48.8307778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:48.8308324Z boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:08:48.8308615Z 2025-03-04T21:08:48.8309037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:48.8309563Z getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:48.8309882Z getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:48.8310219Z widths: "f32[3230][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:08:48.8310494Z 2025-03-04T21:08:48.8310922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:48.8311491Z getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:48.8311789Z getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:48.8312112Z heights: "f32[3230][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:08:48.8312382Z 2025-03-04T21:08:48.8312791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:48.8313293Z getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:48.8313553Z mul: "f32[3230][1]cpu" = 0.5 * widths 2025-03-04T21:08:48.8313814Z ctr_x: "f32[3230][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:08:48.8314055Z 2025-03-04T21:08:48.8314460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:48.8314978Z getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:48.8315266Z mul_1: "f32[3230][1]cpu" = 0.5 * heights 2025-03-04T21:08:48.8315564Z ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:08:48.8315809Z 2025-03-04T21:08:48.8316240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:48.8316759Z getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:48.8317097Z dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:08:48.8317339Z 2025-03-04T21:08:48.8317734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:48.8318251Z getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:48.8318583Z dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:08:48.8318853Z 2025-03-04T21:08:48.8319254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:48.8319786Z getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:48.8320111Z dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:08:48.8320348Z 2025-03-04T21:08:48.8320772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:48.8321326Z getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:48.8321669Z dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:08:48.8321903Z 2025-03-04T21:08:48.8322340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:48.8322888Z dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:48.8323157Z 2025-03-04T21:08:48.8323592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:48.8324119Z dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:48.8324371Z 2025-03-04T21:08:48.8324790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:48.8325312Z getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:48.8325622Z mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:08:48.8325951Z getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:48.8326297Z pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:08:48.8326559Z 2025-03-04T21:08:48.8326998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:48.8327538Z getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:48.8327855Z mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:08:48.8328182Z getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:48.8328529Z pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:08:48.8328787Z 2025-03-04T21:08:48.8329212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:48.8329717Z exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:48.8330037Z getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:48.8330380Z pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:08:48.8330632Z 2025-03-04T21:08:48.8331053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:48.8331565Z exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:48.8331934Z getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:48.8332300Z pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:08:48.8332585Z 2025-03-04T21:08:48.8333002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:48.8333500Z mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:48.8333783Z x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:48.8334052Z 2025-03-04T21:08:48.8334574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:48.8335068Z mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:48.8335351Z y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:48.8335602Z 2025-03-04T21:08:48.8336021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:48.8336519Z mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:48.8336814Z x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:48.8337068Z 2025-03-04T21:08:48.8337465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:48.8337944Z mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:48.8338235Z y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:48.8338485Z 2025-03-04T21:08:48.8338922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:48.8339507Z pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:48.8339807Z 2025-03-04T21:08:48.8340240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:48.8340810Z predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320)); pred_boxes = None 2025-03-04T21:08:48.8341106Z 2025-03-04T21:08:48.8341565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:08:48.8342198Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:08:48.8342576Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:08:48.8342885Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:08:48.8343206Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:08:48.8343547Z getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:08:48.8343821Z 2025-03-04T21:08:48.8344213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:48.8344803Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:08:48.8345174Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:08:48.8345429Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:08:48.8345845Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:08:48.8346228Z getitem_26: "Sym(1230 - s0)" = size_3[0] 2025-03-04T21:08:48.8346508Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:08:48.8346741Z 2025-03-04T21:08:48.8347234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:08:48.8347872Z probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:08:48.8348242Z 2025-03-04T21:08:48.8348744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:08:48.8349367Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:08:48.8349742Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:08:48.8350046Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:08:48.8350350Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:08:48.8350789Z getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:08:48.8351059Z 2025-03-04T21:08:48.8351613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:48.8352319Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:08:48.8352673Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:48.8353018Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:08:48.8353368Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:48.8353668Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:48.8353910Z 2025-03-04T21:08:48.8354358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:48.8354885Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:48.8355124Z 2025-03-04T21:08:48.8355258Z 2025-03-04T21:08:48.8355351Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:48.8356172Z def forward(self, L_predictions_0_: "f32[3230, 81][81, 1]cpu", L_predictions_1_: "f32[3230, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"): 2025-03-04T21:08:48.8356976Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:08:48.8357208Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:08:48.8357524Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:08:48.8357928Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:08:48.8358333Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:08:48.8358730Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:08:48.8359025Z 2025-03-04T21:08:48.8359435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:48.8359903Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:48.8360174Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:08:48.8360410Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:08:48.8360688Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:48.8360971Z getitem_2: "Sym(1230 - s0)" = size_1[0] 2025-03-04T21:08:48.8361221Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:08:48.8361466Z 2025-03-04T21:08:48.8361844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:48.8362800Z proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:08:48.8363526Z 2025-03-04T21:08:48.8363993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:48.8364584Z deltas: "f32[3230, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:08:48.8364865Z 2025-03-04T21:08:48.8365272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:48.8365807Z boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:08:48.8366090Z 2025-03-04T21:08:48.8366497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:48.8367001Z getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:48.8367310Z getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:48.8367631Z widths: "f32[3230][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:08:48.8367896Z 2025-03-04T21:08:48.8368305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:48.8368807Z getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:48.8369114Z getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:48.8369434Z heights: "f32[3230][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:08:48.8369716Z 2025-03-04T21:08:48.8370125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:48.8370620Z getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:48.8370892Z mul: "f32[3230][1]cpu" = 0.5 * widths 2025-03-04T21:08:48.8371158Z ctr_x: "f32[3230][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:08:48.8371407Z 2025-03-04T21:08:48.8371825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:48.8372373Z getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:48.8372680Z mul_1: "f32[3230][1]cpu" = 0.5 * heights 2025-03-04T21:08:48.8372978Z ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:08:48.8373238Z 2025-03-04T21:08:48.8373667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:48.8374230Z getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:48.8374666Z dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:08:48.8374953Z 2025-03-04T21:08:48.8375398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:48.8375970Z getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:48.8376311Z dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:08:48.8376555Z 2025-03-04T21:08:48.8376960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:48.8377482Z getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:48.8377816Z dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:08:48.8378060Z 2025-03-04T21:08:48.8378471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:48.8379032Z getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:48.8379392Z dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:08:48.8379633Z 2025-03-04T21:08:48.8380073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:48.8380621Z dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:48.8380894Z 2025-03-04T21:08:48.8381325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:48.8381863Z dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:48.8382131Z 2025-03-04T21:08:48.8382578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:48.8383137Z getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:48.8383477Z mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:08:48.8383830Z getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:48.8384209Z pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:08:48.8384474Z 2025-03-04T21:08:48.8384934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:48.8385504Z getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:48.8385839Z mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:08:48.8386185Z getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:48.8386578Z pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:08:48.8386846Z 2025-03-04T21:08:48.8387284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:48.8387838Z exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:48.8388386Z getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:48.8388823Z pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:08:48.8389125Z 2025-03-04T21:08:48.8389580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:48.8390117Z exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:48.8390479Z getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:48.8390859Z pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:08:48.8391141Z 2025-03-04T21:08:48.8391575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:48.8392070Z mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:48.8392360Z x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:48.8392616Z 2025-03-04T21:08:48.8393046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:48.8393542Z mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:48.8393824Z y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:48.8394080Z 2025-03-04T21:08:48.8394503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:48.8395011Z mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:48.8395330Z x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:48.8395602Z 2025-03-04T21:08:48.8396028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:48.8396534Z mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:48.8396846Z y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:48.8397114Z 2025-03-04T21:08:48.8397583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:48.8398207Z pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:48.8398528Z 2025-03-04T21:08:48.8398980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:48.8399576Z predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320)); pred_boxes = None 2025-03-04T21:08:48.8399885Z 2025-03-04T21:08:48.8400363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:08:48.8401042Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:08:48.8401435Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:08:48.8401743Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:08:48.8402108Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:08:48.8402446Z getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:08:48.8402735Z 2025-03-04T21:08:48.8403134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:48.8403715Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:08:48.8404072Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:08:48.8404320Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:08:48.8404680Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:08:48.8405039Z getitem_26: "Sym(1230 - s0)" = size_3[0] 2025-03-04T21:08:48.8405293Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:08:48.8405516Z 2025-03-04T21:08:48.8405942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:08:48.8406544Z probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:08:48.8406876Z 2025-03-04T21:08:48.8407319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:08:48.8407924Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:08:48.8408286Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:08:48.8408578Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:08:48.8408882Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:08:48.8409198Z getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:08:48.8409460Z 2025-03-04T21:08:48.8410013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:48.8410710Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:08:48.8411054Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:48.8411396Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:08:48.8411742Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:48.8412041Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:48.8412283Z 2025-03-04T21:08:48.8412732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:48.8413259Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:48.8413500Z 2025-03-04T21:08:48.8413632Z 2025-03-04T21:08:48.8413726Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:48.8414707Z def forward(self, L_predictions_0_: "f32[3230, 81][81, 1]cpu", L_predictions_1_: "f32[3230, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"): 2025-03-04T21:08:48.8415633Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:08:48.8415902Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:08:48.8416248Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:08:48.8416687Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:08:48.8417114Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:08:48.8417522Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:08:48.8417815Z 2025-03-04T21:08:48.8418213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:48.8418700Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:48.8418966Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:08:48.8419210Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:08:48.8419493Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:48.8419767Z getitem_2: "Sym(1230 - s0)" = size_1[0] 2025-03-04T21:08:48.8420025Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:08:48.8420254Z 2025-03-04T21:08:48.8420641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:48.8421614Z proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:08:48.8422343Z 2025-03-04T21:08:48.8422826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:48.8423418Z deltas: "f32[3230, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:08:48.8423700Z 2025-03-04T21:08:48.8424111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:48.8424655Z boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:08:48.8424946Z 2025-03-04T21:08:48.8425360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:48.8425869Z getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:48.8426185Z getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:48.8426513Z widths: "f32[3230][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:08:48.8426785Z 2025-03-04T21:08:48.8427209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:48.8427721Z getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:48.8428025Z getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:48.8428350Z heights: "f32[3230][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:08:48.8428623Z 2025-03-04T21:08:48.8429054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:48.8429576Z getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:48.8429845Z mul: "f32[3230][1]cpu" = 0.5 * widths 2025-03-04T21:08:48.8430111Z ctr_x: "f32[3230][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:08:48.8430355Z 2025-03-04T21:08:48.8430788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:48.8431335Z getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:48.8431634Z mul_1: "f32[3230][1]cpu" = 0.5 * heights 2025-03-04T21:08:48.8431915Z ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:08:48.8432172Z 2025-03-04T21:08:48.8432586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:48.8433124Z getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:48.8433465Z dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:08:48.8433706Z 2025-03-04T21:08:48.8434113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:48.8434642Z getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:48.8434976Z dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:08:48.8435220Z 2025-03-04T21:08:48.8435628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:48.8436153Z getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:48.8436491Z dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:08:48.8436735Z 2025-03-04T21:08:48.8437150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:48.8437718Z getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:48.8438083Z dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:08:48.8438326Z 2025-03-04T21:08:48.8438766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:48.8439318Z dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:48.8439589Z 2025-03-04T21:08:48.8440027Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:48.8440570Z dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:48.8440838Z 2025-03-04T21:08:48.8441301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:48.8441880Z getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:48.8442211Z mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:08:48.8442580Z getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:48.8442940Z pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:08:48.8443229Z 2025-03-04T21:08:48.8443693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:48.8444285Z getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:48.8444638Z mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:08:48.8444984Z getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:48.8445344Z pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:08:48.8445614Z 2025-03-04T21:08:48.8446063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:48.8446598Z exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:48.8446944Z getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:48.8447309Z pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:08:48.8447576Z 2025-03-04T21:08:48.8448023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:48.8448558Z exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:48.8448910Z getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:48.8460900Z pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:08:48.8461326Z 2025-03-04T21:08:48.8461813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:48.8462321Z mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:48.8462610Z x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:48.8462867Z 2025-03-04T21:08:48.8463284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:48.8463763Z mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:48.8464033Z y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:48.8464277Z 2025-03-04T21:08:48.8464686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:48.8465177Z mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:48.8465480Z x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:48.8465739Z 2025-03-04T21:08:48.8466147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:48.8466625Z mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:48.8466924Z y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:48.8467179Z 2025-03-04T21:08:48.8467703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:48.8468308Z pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:48.8468645Z 2025-03-04T21:08:48.8469082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:48.8469685Z predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320)); pred_boxes = None 2025-03-04T21:08:48.8469980Z 2025-03-04T21:08:48.8470462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:08:48.8471084Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:08:48.8471462Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:08:48.8471763Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:08:48.8472076Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:08:48.8472404Z getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:08:48.8472676Z 2025-03-04T21:08:48.8473071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:48.8473643Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:08:48.8474002Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:08:48.8474255Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:08:48.8474632Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:08:48.8474988Z getitem_26: "Sym(1230 - s0)" = size_3[0] 2025-03-04T21:08:48.8475239Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:08:48.8475466Z 2025-03-04T21:08:48.8475898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:08:48.8476499Z probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:08:48.8476830Z 2025-03-04T21:08:48.8477281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:08:48.8477941Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:08:48.8478329Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:08:48.8478625Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:08:48.8478933Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:08:48.8479246Z getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:08:48.8479509Z 2025-03-04T21:08:48.8480066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:48.8480764Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:08:48.8481116Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:48.8481485Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:08:48.8481836Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:48.8482167Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:48.8482416Z 2025-03-04T21:08:48.8482863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:48.8483406Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:48.8483666Z 2025-03-04T21:08:51.1466941Z 2025-03-04T21:08:51.1469597Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:51.1473906Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T21:08:51.1477919Z l_scores_0_ = L_scores_0_ 2025-03-04T21:08:51.1480213Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:08:51.1485282Z 2025-03-04T21:08:51.1486118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:51.1486939Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:08:51.1492752Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:51.1495041Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:08:51.1495481Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:51.1495833Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:51.1496118Z 2025-03-04T21:08:51.1496681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:51.1497241Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:51.1497508Z 2025-03-04T21:08:51.1497602Z 2025-03-04T21:08:51.1497693Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:51.1498004Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T21:08:51.1498312Z l_scores_0_ = L_scores_0_ 2025-03-04T21:08:51.1498529Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:08:51.1498734Z 2025-03-04T21:08:51.1499300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:51.1499981Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:08:51.1500309Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:51.1500629Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:08:51.1500950Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:51.1501245Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:51.1501484Z 2025-03-04T21:08:51.1501935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:51.1502459Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:51.1502693Z 2025-03-04T21:09:07.5426515Z Compilation time (from dynamo_timed): 36.140750541 2025-03-04T21:09:07.5430984Z pass 2025-03-04T21:09:07.5433917Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:09:07.5439171Z TIMING: entire_frame_compile:36.14075 gc:0.03576 _recursive_pre_grad_passes:0.02915 async_compile.wait:8.51733 backend_compile:23.79273 _recursive_joint_graph_passes:0.46241 _recursive_post_grad_passes:0.08177 code_gen:11.54156 inductor_compile:13.06862 total_wall_time:36.14075 2025-03-04T21:09:07.5443366Z STATS: call_* op count: 611 | FakeTensorMode.__torch_dispatch__:18544 | FakeTensor.__torch_dispatch__:1848 | ProxyTorchDispatchMode.__torch_dispatch__:5928 | attempt fast:51 | slow no contiguity match:20 | fast is_contiguous:31 2025-03-04T21:09:07.5444050Z Dynamo produced 52 graphs covering 611 ops with 42 graph breaks (6 unique) 2025-03-04T21:09:13.2441668Z 2025-03-04T21:09:20.4341801Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:09:20.4342237Z loading model: 0it [00:07, ?it/s] 2025-03-04T21:09:20.4353634Z cpu eval detectron2_fasterrcnn_r_50_fpn 2025-03-04T21:09:34.9574732Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_50_fpn. Setting accuracy check to cosine 2025-03-04T21:09:34.9833225Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:09:43.3938563Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:09:52.1222394Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:10:02.6405934Z 2025-03-04T21:10:02.6406529Z class GraphModule(torch.nn.Module): 2025-03-04T21:10:02.6480014Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", 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L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:10:02.6547101Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:10:02.6547622Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.6548416Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6549244Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6550041Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6550817Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6551573Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.6552375Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6553235Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6554063Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6554865Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6555635Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.6556491Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6557366Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6558221Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6559050Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6559826Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.6560631Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6561491Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6562327Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6563126Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6563914Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:10:02.6564754Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6565630Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6566491Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6567324Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6568130Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.6568950Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6569843Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6570679Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6571537Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6572322Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.6573166Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6574041Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6575130Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6576046Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6576896Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.6577768Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6578697Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6579625Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6580520Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6581371Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.6582248Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6583180Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6584089Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6584988Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6585817Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.6586711Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6587648Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6588648Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6589524Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6590363Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.6591224Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6592154Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6593063Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6593937Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6594768Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.6595635Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6596545Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6597370Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6598193Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6598979Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.6599814Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6600685Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6601537Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6602357Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6603149Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.6603976Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6604866Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6605720Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6606538Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6607337Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:10:02.6608213Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6609172Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6610105Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6611001Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6611845Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.6612712Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6613661Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6614617Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6615601Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6616513Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.6617383Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6618319Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6619229Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6620106Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6620965Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.6621841Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6622767Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6623673Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6624544Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6625398Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.6626221Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6627104Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6627955Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6628840Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6629639Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.6630477Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6631405Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6632287Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6633137Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6633951Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.6634783Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6635667Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6636522Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6637350Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6638141Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.6638962Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6639837Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6640689Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6641503Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6642313Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.6643141Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6644052Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6644882Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6645701Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6646481Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.6647289Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6648142Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6648979Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6649803Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6650604Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.6651437Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6652325Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6653178Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6654010Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6654958Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.6655915Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6656936Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6657891Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6658849Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6659745Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.6660679Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6661664Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6662618Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6663527Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6664427Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:10:02.6665353Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6666250Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6667125Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6667983Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6668787Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.6669610Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6670475Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6671344Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6672164Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6672973Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.6673805Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6674693Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6675544Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6676371Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6677160Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.6677979Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6678857Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6679725Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6680553Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6681345Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.6682166Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6683044Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6683889Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6684713Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6685535Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.6686359Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6687296Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6688382Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6689268Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6690163Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.6691092Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6692096Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6693055Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6693978Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6694950Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.6695921Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6696931Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6697880Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6698774Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6699665Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.6700537Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6701547Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6702475Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6703340Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6704160Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.6704974Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6705828Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6706661Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6707484Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6708270Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.6709092Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6709964Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6710811Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6711634Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6712419Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.6713238Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6714087Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6714924Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6715723Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6716524Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.6717331Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6718216Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6719053Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6719865Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6720650Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.6721461Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6722333Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6723175Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6723993Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6724778Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.6725592Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6726466Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6727319Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6728140Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6728944Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.6729782Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6730679Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6731550Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6732383Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6733173Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.6733996Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6735052Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6736046Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6736930Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6737790Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.6738679Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6739625Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6740559Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6741454Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6742290Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.6743195Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6744118Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6745047Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6745884Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6746706Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:10:02.6747559Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6748459Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6749336Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6750190Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6751005Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.6751831Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6752713Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6753567Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6754394Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6755188Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.6756012Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6756893Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6757776Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6758613Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6759422Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.6760266Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6761131Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6761963Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6762769Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6763566Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.6764369Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6765223Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6766073Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6766894Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6767689Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.6768509Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6769391Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6770241Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6771059Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6771867Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.6772703Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.6773594Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.6774455Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.6775484Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.6776269Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T21:10:02.6776852Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T21:10:02.6777414Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T21:10:02.6777968Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T21:10:02.6778530Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T21:10:02.6779093Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T21:10:02.6779643Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T21:10:02.6780198Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T21:10:02.6780754Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T21:10:02.6781304Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T21:10:02.6781854Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T21:10:02.6782401Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T21:10:02.6782955Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T21:10:02.6783502Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T21:10:02.6784048Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T21:10:02.6784593Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T21:10:02.6785278Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:10:02.6786065Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:10:02.6786823Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:10:02.6787600Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:10:02.6788605Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:10:02.6789424Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:10:02.6790148Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:10:02.6790894Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:10:02.6791687Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:10:02.6792461Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:10:02.6793225Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:10:02.6793708Z 2025-03-04T21:10:02.6794123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6795013Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.6795712Z 2025-03-04T21:10:02.6796086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6798183Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6800033Z 2025-03-04T21:10:02.6800398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:10:02.6800905Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:10:02.6801170Z 2025-03-04T21:10:02.6801614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:10:02.6802282Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:10:02.6802631Z 2025-03-04T21:10:02.6802987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6803787Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.6804376Z 2025-03-04T21:10:02.6804731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6806792Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6808643Z 2025-03-04T21:10:02.6809011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6809492Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:10:02.6809745Z 2025-03-04T21:10:02.6810081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6810871Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.6811482Z 2025-03-04T21:10:02.6811833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6813978Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6816050Z 2025-03-04T21:10:02.6816437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6816983Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:10:02.6817295Z 2025-03-04T21:10:02.6817676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6818602Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.6819310Z 2025-03-04T21:10:02.6819711Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6822062Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6823997Z 2025-03-04T21:10:02.6824346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6825162Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:10:02.6825782Z 2025-03-04T21:10:02.6826142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6828353Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6830494Z 2025-03-04T21:10:02.6830871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.6831365Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:10:02.6831655Z 2025-03-04T21:10:02.6832035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6832547Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:10:02.6832818Z 2025-03-04T21:10:02.6833159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6833953Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.6834555Z 2025-03-04T21:10:02.6834912Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6837055Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6838965Z 2025-03-04T21:10:02.6839340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6839836Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:10:02.6840105Z 2025-03-04T21:10:02.6840452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6841235Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.6841830Z 2025-03-04T21:10:02.6842175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6844261Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6846142Z 2025-03-04T21:10:02.6846500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6846993Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:10:02.6847258Z 2025-03-04T21:10:02.6847600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6848412Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.6849026Z 2025-03-04T21:10:02.6849377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6851497Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6853385Z 2025-03-04T21:10:02.6853754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.6854252Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:10:02.6854585Z 2025-03-04T21:10:02.6855010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6855560Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:10:02.6855862Z 2025-03-04T21:10:02.6856246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6857138Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.6857860Z 2025-03-04T21:10:02.6858274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6860643Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6862747Z 2025-03-04T21:10:02.6863167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6863723Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:10:02.6864018Z 2025-03-04T21:10:02.6864390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6865316Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.6866010Z 2025-03-04T21:10:02.6866403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6868618Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6870485Z 2025-03-04T21:10:02.6870864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6871350Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:10:02.6871616Z 2025-03-04T21:10:02.6871963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6872802Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.6873447Z 2025-03-04T21:10:02.6873811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6875971Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6878330Z 2025-03-04T21:10:02.6878733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.6879228Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:10:02.6879537Z 2025-03-04T21:10:02.6880107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6880844Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:10:02.6881220Z 2025-03-04T21:10:02.6881726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6882969Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.6883921Z 2025-03-04T21:10:02.6884436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6887736Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6890924Z 2025-03-04T21:10:02.6891450Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6892210Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:10:02.6892635Z 2025-03-04T21:10:02.6893004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6893904Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.6894744Z 2025-03-04T21:10:02.6895150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6897481Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6899627Z 2025-03-04T21:10:02.6900052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6900603Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:10:02.6900904Z 2025-03-04T21:10:02.6901280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6902174Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.6902859Z 2025-03-04T21:10:02.6903261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6906621Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6909045Z 2025-03-04T21:10:02.6909439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6910398Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:10:02.6911040Z 2025-03-04T21:10:02.6911395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6913602Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6915716Z 2025-03-04T21:10:02.6916089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.6916584Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:10:02.6916863Z 2025-03-04T21:10:02.6917240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6917745Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:10:02.6918023Z 2025-03-04T21:10:02.6918370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6919186Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.6919796Z 2025-03-04T21:10:02.6920165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6922364Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6924418Z 2025-03-04T21:10:02.6924794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6925307Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:10:02.6925628Z 2025-03-04T21:10:02.6925966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6926905Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.6927513Z 2025-03-04T21:10:02.6927867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6929989Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6931964Z 2025-03-04T21:10:02.6932355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6932870Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:10:02.6933146Z 2025-03-04T21:10:02.6933503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6934364Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.6935149Z 2025-03-04T21:10:02.6935557Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6937867Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6939918Z 2025-03-04T21:10:02.6940320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.6940874Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:10:02.6941175Z 2025-03-04T21:10:02.6941571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6942098Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:10:02.6942387Z 2025-03-04T21:10:02.6942748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6943600Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.6944246Z 2025-03-04T21:10:02.6944619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6946761Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6948651Z 2025-03-04T21:10:02.6949031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6949518Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:10:02.6949782Z 2025-03-04T21:10:02.6950117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6950932Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.6951551Z 2025-03-04T21:10:02.6951923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6954067Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6955984Z 2025-03-04T21:10:02.6956370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6956851Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:10:02.6957111Z 2025-03-04T21:10:02.6957447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6958238Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.6958838Z 2025-03-04T21:10:02.6959181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6961285Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6963156Z 2025-03-04T21:10:02.6963519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.6964001Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:10:02.6964273Z 2025-03-04T21:10:02.6964639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6965120Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:10:02.6965392Z 2025-03-04T21:10:02.6965745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6966530Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.6967151Z 2025-03-04T21:10:02.6967514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6969577Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6971473Z 2025-03-04T21:10:02.6971855Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6972351Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:10:02.6972618Z 2025-03-04T21:10:02.6972951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6973777Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.6974399Z 2025-03-04T21:10:02.6974842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6977295Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6979362Z 2025-03-04T21:10:02.6979762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6980303Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:10:02.6980591Z 2025-03-04T21:10:02.6980956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6981844Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.6982466Z 2025-03-04T21:10:02.6982870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6985088Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6987047Z 2025-03-04T21:10:02.6987423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.6987919Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:10:02.6988452Z 2025-03-04T21:10:02.6988905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6989445Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:10:02.6989728Z 2025-03-04T21:10:02.6990072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6990881Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.6991482Z 2025-03-04T21:10:02.6991842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.6994040Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.6995976Z 2025-03-04T21:10:02.6996355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.6996872Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:10:02.6997146Z 2025-03-04T21:10:02.6997524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.6998361Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.6998996Z 2025-03-04T21:10:02.6999359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7001552Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7003437Z 2025-03-04T21:10:02.7003802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7004278Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:10:02.7004540Z 2025-03-04T21:10:02.7004871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7005658Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7006255Z 2025-03-04T21:10:02.7006603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7008696Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7010521Z 2025-03-04T21:10:02.7010875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7011673Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:10:02.7012273Z 2025-03-04T21:10:02.7012611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7014890Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7017303Z 2025-03-04T21:10:02.7017697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7018180Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:10:02.7018442Z 2025-03-04T21:10:02.7018822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7019315Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:10:02.7019580Z 2025-03-04T21:10:02.7019922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7020718Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7021316Z 2025-03-04T21:10:02.7021673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7023817Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7025723Z 2025-03-04T21:10:02.7026096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7026576Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:10:02.7026837Z 2025-03-04T21:10:02.7027167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7027969Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7028581Z 2025-03-04T21:10:02.7028938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7031030Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7032919Z 2025-03-04T21:10:02.7033291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7033770Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:10:02.7034028Z 2025-03-04T21:10:02.7034369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7035171Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7035775Z 2025-03-04T21:10:02.7036130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7038254Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7040094Z 2025-03-04T21:10:02.7040454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7040931Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:10:02.7041194Z 2025-03-04T21:10:02.7041560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7042035Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:10:02.7042296Z 2025-03-04T21:10:02.7042628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7043402Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7043982Z 2025-03-04T21:10:02.7044342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7046381Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7048198Z 2025-03-04T21:10:02.7048564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7049037Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:10:02.7049292Z 2025-03-04T21:10:02.7049632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7050438Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7051024Z 2025-03-04T21:10:02.7051368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7053460Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7055651Z 2025-03-04T21:10:02.7056081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7056653Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:10:02.7056953Z 2025-03-04T21:10:02.7057343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7058298Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7059009Z 2025-03-04T21:10:02.7059418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7061845Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7063747Z 2025-03-04T21:10:02.7064115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7064603Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:10:02.7064867Z 2025-03-04T21:10:02.7065241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7065752Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:10:02.7066021Z 2025-03-04T21:10:02.7066362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7067948Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7068583Z 2025-03-04T21:10:02.7068950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7071077Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7073010Z 2025-03-04T21:10:02.7073388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7073878Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:10:02.7074150Z 2025-03-04T21:10:02.7074495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7075301Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7075912Z 2025-03-04T21:10:02.7076271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7078398Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7080277Z 2025-03-04T21:10:02.7080658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7081130Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:10:02.7081402Z 2025-03-04T21:10:02.7081737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7082541Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7083155Z 2025-03-04T21:10:02.7083494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7085582Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7087488Z 2025-03-04T21:10:02.7087856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7088502Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:10:02.7088777Z 2025-03-04T21:10:02.7089153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7089644Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:10:02.7089907Z 2025-03-04T21:10:02.7090248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7091055Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7091653Z 2025-03-04T21:10:02.7092006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7094191Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7096286Z 2025-03-04T21:10:02.7096712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7097255Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:10:02.7097533Z 2025-03-04T21:10:02.7097888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7098749Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7099391Z 2025-03-04T21:10:02.7099750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7101857Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7103736Z 2025-03-04T21:10:02.7104116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7104604Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:10:02.7104863Z 2025-03-04T21:10:02.7105207Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7106008Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7106613Z 2025-03-04T21:10:02.7106970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7109092Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7110978Z 2025-03-04T21:10:02.7111337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7111809Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:10:02.7112073Z 2025-03-04T21:10:02.7112437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7112910Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:10:02.7113166Z 2025-03-04T21:10:02.7113501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7114280Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7114859Z 2025-03-04T21:10:02.7115207Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7117278Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7119116Z 2025-03-04T21:10:02.7119479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7119950Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:10:02.7120206Z 2025-03-04T21:10:02.7120537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7121315Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7121905Z 2025-03-04T21:10:02.7122278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7124342Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7126205Z 2025-03-04T21:10:02.7126569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7127029Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:10:02.7127281Z 2025-03-04T21:10:02.7127607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7128381Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7128983Z 2025-03-04T21:10:02.7129340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7131438Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7133320Z 2025-03-04T21:10:02.7133690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7134181Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:10:02.7134555Z 2025-03-04T21:10:02.7135020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7135604Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:10:02.7135883Z 2025-03-04T21:10:02.7136253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7137055Z x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7137671Z 2025-03-04T21:10:02.7138050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7140197Z x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7142063Z 2025-03-04T21:10:02.7142438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7142916Z out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:10:02.7143178Z 2025-03-04T21:10:02.7143517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7144311Z x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7144913Z 2025-03-04T21:10:02.7145267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7147374Z x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7149233Z 2025-03-04T21:10:02.7149594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7150085Z out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:10:02.7150331Z 2025-03-04T21:10:02.7150666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7151514Z x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7152126Z 2025-03-04T21:10:02.7152472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7154522Z x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7156347Z 2025-03-04T21:10:02.7156681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7157459Z x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:10:02.7158054Z 2025-03-04T21:10:02.7158401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7160508Z x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7162554Z 2025-03-04T21:10:02.7162933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7163404Z x_93 += x_95; out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93; x_93 = x_95 = None 2025-03-04T21:10:02.7163663Z 2025-03-04T21:10:02.7164055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7164555Z out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:10:02.7164817Z 2025-03-04T21:10:02.7165156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7165970Z x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7166578Z 2025-03-04T21:10:02.7166937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7169046Z x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7170947Z 2025-03-04T21:10:02.7171320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7171803Z out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:10:02.7172066Z 2025-03-04T21:10:02.7172413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7173252Z x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7173888Z 2025-03-04T21:10:02.7174263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7176769Z x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7178653Z 2025-03-04T21:10:02.7179067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7179551Z out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99); x_99 = None 2025-03-04T21:10:02.7179809Z 2025-03-04T21:10:02.7180171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7181001Z x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7181619Z 2025-03-04T21:10:02.7181975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7184110Z x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7186012Z 2025-03-04T21:10:02.7186389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7186889Z x_101 += out_55; out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101; x_101 = out_55 = None 2025-03-04T21:10:02.7187165Z 2025-03-04T21:10:02.7187541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7188032Z out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:10:02.7188465Z 2025-03-04T21:10:02.7188843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7189641Z x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7190251Z 2025-03-04T21:10:02.7190611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7192860Z x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7194882Z 2025-03-04T21:10:02.7195283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7195795Z out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:10:02.7196080Z 2025-03-04T21:10:02.7196441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7197297Z x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7197940Z 2025-03-04T21:10:02.7198326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7200591Z x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7202612Z 2025-03-04T21:10:02.7203006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7203491Z out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105); x_105 = None 2025-03-04T21:10:02.7203755Z 2025-03-04T21:10:02.7204102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7204917Z x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7205531Z 2025-03-04T21:10:02.7205882Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7208058Z x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7209959Z 2025-03-04T21:10:02.7210325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7210814Z x_107 += out_59; out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107; x_107 = out_59 = None 2025-03-04T21:10:02.7211086Z 2025-03-04T21:10:02.7211457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7211943Z out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:10:02.7212208Z 2025-03-04T21:10:02.7212548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7213432Z x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T21:10:02.7214112Z 2025-03-04T21:10:02.7214536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7215523Z x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T21:10:02.7216256Z 2025-03-04T21:10:02.7216761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:10:02.7217493Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest'); x_108 = None 2025-03-04T21:10:02.7217883Z 2025-03-04T21:10:02.7218225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7219094Z x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T21:10:02.7219779Z 2025-03-04T21:10:02.7220218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:10:02.7220843Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features; x_110 = top_down_features = None 2025-03-04T21:10:02.7221161Z 2025-03-04T21:10:02.7221527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7222439Z x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T21:10:02.7223149Z 2025-03-04T21:10:02.7223644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:10:02.7224436Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T21:10:02.7224874Z 2025-03-04T21:10:02.7225217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7226109Z x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T21:10:02.7226797Z 2025-03-04T21:10:02.7227217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:10:02.7227824Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1; x_112 = top_down_features_1 = None 2025-03-04T21:10:02.7228148Z 2025-03-04T21:10:02.7228477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7229358Z x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T21:10:02.7230045Z 2025-03-04T21:10:02.7230524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:10:02.7231290Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T21:10:02.7231725Z 2025-03-04T21:10:02.7232059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7232921Z x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T21:10:02.7233596Z 2025-03-04T21:10:02.7234051Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:10:02.7234654Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2; x_114 = top_down_features_2 = None 2025-03-04T21:10:02.7234997Z 2025-03-04T21:10:02.7235327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7236240Z x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T21:10:02.7236971Z 2025-03-04T21:10:02.7237419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T21:10:02.7238031Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T21:10:02.7238353Z 2025-03-04T21:10:02.7238872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7239504Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:10:02.7239772Z 2025-03-04T21:10:02.7240150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7240635Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:10:02.7240889Z 2025-03-04T21:10:02.7241398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7242028Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:10:02.7242299Z 2025-03-04T21:10:02.7242684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7243181Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:10:02.7243448Z 2025-03-04T21:10:02.7243900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:02.7244494Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:10:02.7244823Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:10:02.7245092Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:10:02.7245324Z 2025-03-04T21:10:02.7245733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:02.7246239Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:10:02.7246477Z 2025-03-04T21:10:02.7246882Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:02.7247376Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:10:02.7247641Z 2025-03-04T21:10:02.7248105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:02.7248756Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:10:02.7249078Z 2025-03-04T21:10:02.7249593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:02.7250200Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:10:02.7250804Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:10:02.7251400Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:10:02.7251701Z x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:10:02.7251940Z 2025-03-04T21:10:02.7252458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7253079Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:10:02.7253350Z 2025-03-04T21:10:02.7253728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7254217Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:10:02.7254528Z 2025-03-04T21:10:02.7255087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7255760Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:10:02.7256049Z 2025-03-04T21:10:02.7256448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7256956Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:10:02.7257231Z 2025-03-04T21:10:02.7257710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:02.7258358Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:10:02.7258729Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:10:02.7259028Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:10:02.7259287Z 2025-03-04T21:10:02.7259722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:02.7260254Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:10:02.7260514Z 2025-03-04T21:10:02.7260948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:02.7261499Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:10:02.7261754Z 2025-03-04T21:10:02.7262229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:02.7262918Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:10:02.7263260Z 2025-03-04T21:10:02.7263793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:02.7264421Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:10:02.7265050Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:10:02.7265666Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:10:02.7265976Z x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:10:02.7266222Z 2025-03-04T21:10:02.7266760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7267411Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:10:02.7267686Z 2025-03-04T21:10:02.7268081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7268588Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:10:02.7268862Z 2025-03-04T21:10:02.7269402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7270054Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:10:02.7270330Z 2025-03-04T21:10:02.7270725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7271231Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:10:02.7271503Z 2025-03-04T21:10:02.7271978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:02.7272625Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:10:02.7272988Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:10:02.7273272Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:10:02.7273520Z 2025-03-04T21:10:02.7273951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:02.7274490Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:10:02.7274750Z 2025-03-04T21:10:02.7275185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:02.7275738Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:10:02.7275985Z 2025-03-04T21:10:02.7276469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:02.7277167Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:10:02.7277510Z 2025-03-04T21:10:02.7278024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:02.7278673Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:10:02.7279282Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:10:02.7279888Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:10:02.7280186Z x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T21:10:02.7280421Z 2025-03-04T21:10:02.7280943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7281579Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:10:02.7281840Z 2025-03-04T21:10:02.7282224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7282718Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:10:02.7282977Z 2025-03-04T21:10:02.7283493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7284121Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:10:02.7284388Z 2025-03-04T21:10:02.7284769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7285251Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:10:02.7285510Z 2025-03-04T21:10:02.7285980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:02.7286602Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:10:02.7286958Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:10:02.7287234Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:10:02.7287477Z 2025-03-04T21:10:02.7287899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:02.7288587Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:10:02.7288844Z 2025-03-04T21:10:02.7289325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:02.7289850Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:10:02.7290100Z 2025-03-04T21:10:02.7290614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:02.7291313Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:10:02.7291658Z 2025-03-04T21:10:02.7292198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:02.7292816Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:10:02.7293441Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:10:02.7294058Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:10:02.7294365Z x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T21:10:02.7294672Z 2025-03-04T21:10:02.7295227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7295902Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:10:02.7296195Z 2025-03-04T21:10:02.7296596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7297105Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:10:02.7297382Z 2025-03-04T21:10:02.7297921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7298578Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:10:02.7298853Z 2025-03-04T21:10:02.7299244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7299740Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:10:02.7300009Z 2025-03-04T21:10:02.7300487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:02.7301127Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:10:02.7301486Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:10:02.7301770Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:10:02.7302020Z 2025-03-04T21:10:02.7302451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:02.7302982Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:10:02.7303238Z 2025-03-04T21:10:02.7303695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:02.7304215Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:10:02.7304487Z 2025-03-04T21:10:02.7304974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:02.7305657Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:10:02.7306014Z 2025-03-04T21:10:02.7306533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:02.7307146Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:10:02.7307769Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:10:02.7308388Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:10:02.7308696Z x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T21:10:02.7308939Z 2025-03-04T21:10:02.7309352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:10:02.7309843Z tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32); x_116 = None 2025-03-04T21:10:02.7310159Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32); x_117 = None 2025-03-04T21:10:02.7310465Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32); x_118 = None 2025-03-04T21:10:02.7310774Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32); x_119 = None 2025-03-04T21:10:02.7311078Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32); x_120 = None 2025-03-04T21:10:02.7311322Z 2025-03-04T21:10:02.7311675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7312193Z x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_115 = None 2025-03-04T21:10:02.7312264Z 2025-03-04T21:10:02.7312554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:02.7312757Z x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False); x_121 = None 2025-03-04T21:10:02.7312832Z 2025-03-04T21:10:02.7313218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:02.7313748Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:02.7313821Z 2025-03-04T21:10:02.7314181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:02.7314739Z x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_122 = None 2025-03-04T21:10:02.7314822Z 2025-03-04T21:10:02.7315104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7315590Z x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_113 = None 2025-03-04T21:10:02.7315686Z 2025-03-04T21:10:02.7315962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:02.7316166Z x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False); x_123 = None 2025-03-04T21:10:02.7316232Z 2025-03-04T21:10:02.7316614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:02.7317135Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:02.7317201Z 2025-03-04T21:10:02.7317565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:02.7318079Z x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_124 = None 2025-03-04T21:10:02.7318152Z 2025-03-04T21:10:02.7318406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7318880Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_111 = None 2025-03-04T21:10:02.7318947Z 2025-03-04T21:10:02.7319228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:02.7319414Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False); x_125 = None 2025-03-04T21:10:02.7319487Z 2025-03-04T21:10:02.7319858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:02.7320360Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:02.7320432Z 2025-03-04T21:10:02.7320805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:02.7321307Z x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_126 = None 2025-03-04T21:10:02.7321389Z 2025-03-04T21:10:02.7321687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7322140Z x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_109 = None 2025-03-04T21:10:02.7322235Z 2025-03-04T21:10:02.7322506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:02.7322691Z x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False); x_127 = None 2025-03-04T21:10:02.7322755Z 2025-03-04T21:10:02.7323124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:02.7323608Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:02.7323679Z 2025-03-04T21:10:02.7324026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:02.7324515Z x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_128 = None 2025-03-04T21:10:02.7324587Z 2025-03-04T21:10:02.7324834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7325584Z x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:10:02.7325649Z 2025-03-04T21:10:02.7325921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:02.7326093Z x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False); x_129 = None 2025-03-04T21:10:02.7326165Z 2025-03-04T21:10:02.7326532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:02.7327378Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:10:02.7327463Z 2025-03-04T21:10:02.7327808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:02.7328619Z x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:10:02.7328699Z 2025-03-04T21:10:02.7329038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:10:02.7329203Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:10:02.7329353Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:10:02.7329515Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:10:02.7329666Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:10:02.7329816Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:10:02.7329961Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:10:02.7330107Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:10:02.7330247Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:10:02.7330390Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:10:02.7330527Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:10:02.7330591Z 2025-03-04T21:10:02.7331009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:10:02.7331194Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304); x_131 = None 2025-03-04T21:10:02.7331380Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:10:02.7331560Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:10:02.7331724Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152); x_132 = None 2025-03-04T21:10:02.7331902Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:10:02.7332071Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:10:02.7332225Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76); x_133 = None 2025-03-04T21:10:02.7332387Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:10:02.7332576Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:10:02.7332720Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38); x_134 = None 2025-03-04T21:10:02.7332901Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:10:02.7333064Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:10:02.7333223Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19); x_135 = None 2025-03-04T21:10:02.7333383Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:10:02.7333571Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:10:02.7333636Z 2025-03-04T21:10:02.7334047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:02.7334247Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:10:02.7334325Z 2025-03-04T21:10:02.7334864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:02.7335049Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:10:02.7335209Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:10:02.7335367Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:10:02.7335437Z 2025-03-04T21:10:02.7335849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:02.7336039Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:10:02.7336109Z 2025-03-04T21:10:02.7336453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:02.7336604Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:10:02.7336683Z 2025-03-04T21:10:02.7337025Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:02.7337171Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:10:02.7337303Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7337465Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:10:02.7337532Z 2025-03-04T21:10:02.7337859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:02.7337988Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:10:02.7338123Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:10:02.7338291Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:10:02.7338366Z 2025-03-04T21:10:02.7338694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:02.7338853Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7338951Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:10:02.7339146Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:10:02.7339215Z 2025-03-04T21:10:02.7339568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:02.7339727Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:10:02.7339849Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:10:02.7339987Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:10:02.7340062Z 2025-03-04T21:10:02.7340428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:02.7340604Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:02.7340729Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:10:02.7340805Z 2025-03-04T21:10:02.7341126Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:02.7341302Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:02.7341425Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:10:02.7341503Z 2025-03-04T21:10:02.7341817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:02.7341991Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:02.7342111Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:10:02.7342190Z 2025-03-04T21:10:02.7342508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:02.7342714Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:10:02.7342839Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:10:02.7342910Z 2025-03-04T21:10:02.7343271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:02.7343427Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:10:02.7343502Z 2025-03-04T21:10:02.7343854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:02.7344010Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:10:02.7344079Z 2025-03-04T21:10:02.7344459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:02.7344608Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:10:02.7344748Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:10:02.7344914Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:10:02.7345089Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:10:02.7345157Z 2025-03-04T21:10:02.7345535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:02.7345710Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:10:02.7345867Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:10:02.7346031Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:10:02.7346209Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:10:02.7346276Z 2025-03-04T21:10:02.7346618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:02.7346739Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:10:02.7346919Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:10:02.7347056Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:10:02.7347130Z 2025-03-04T21:10:02.7347464Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:02.7347592Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:10:02.7347759Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:10:02.7347905Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:10:02.7347971Z 2025-03-04T21:10:02.7348290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:02.7348392Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:10:02.7348520Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:10:02.7348586Z 2025-03-04T21:10:02.7348907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:02.7349005Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:10:02.7349131Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:10:02.7349196Z 2025-03-04T21:10:02.7349511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:02.7349628Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:10:02.7349769Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:10:02.7349837Z 2025-03-04T21:10:02.7350149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:02.7350264Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:10:02.7350399Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:10:02.7350465Z 2025-03-04T21:10:02.7350817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:02.7351017Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:10:02.7351093Z 2025-03-04T21:10:02.7351427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:02.7351616Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:10:02.7351688Z 2025-03-04T21:10:02.7352080Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:02.7352284Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:10:02.7352350Z 2025-03-04T21:10:02.7352758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:02.7352968Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:10:02.7353044Z 2025-03-04T21:10:02.7353478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:02.7353643Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:10:02.7353797Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:10:02.7353943Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:10:02.7354007Z 2025-03-04T21:10:02.7354387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:02.7354561Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:10:02.7354632Z 2025-03-04T21:10:02.7354947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:02.7355102Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:10:02.7355167Z 2025-03-04T21:10:02.7355489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:02.7355622Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:10:02.7355759Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7355910Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:10:02.7355983Z 2025-03-04T21:10:02.7356303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:02.7356439Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:10:02.7356563Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:10:02.7356726Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:10:02.7356791Z 2025-03-04T21:10:02.7357111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:02.7357256Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7357361Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:10:02.7357511Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:10:02.7357584Z 2025-03-04T21:10:02.7357916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:02.7358077Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:10:02.7358212Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:10:02.7358351Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:10:02.7358416Z 2025-03-04T21:10:02.7358737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:02.7358901Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:02.7359020Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:10:02.7359092Z 2025-03-04T21:10:02.7359399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:02.7359559Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:02.7359677Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:10:02.7359751Z 2025-03-04T21:10:02.7360054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:02.7360214Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:02.7360326Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:10:02.7360399Z 2025-03-04T21:10:02.7360707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:02.7360902Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:10:02.7361014Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:10:02.7361087Z 2025-03-04T21:10:02.7361429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:02.7361581Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:10:02.7361647Z 2025-03-04T21:10:02.7361993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:02.7362136Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:10:02.7362210Z 2025-03-04T21:10:02.7362571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:02.7362717Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:10:02.7362842Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:10:02.7363022Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:10:02.7363165Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:10:02.7363237Z 2025-03-04T21:10:02.7363591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:02.7363733Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:10:02.7363872Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:10:02.7364047Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:10:02.7364191Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:10:02.7364255Z 2025-03-04T21:10:02.7364585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:02.7364700Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:10:02.7364868Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:10:02.7365002Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:10:02.7365073Z 2025-03-04T21:10:02.7365399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:02.7365521Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:10:02.7365685Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:10:02.7365825Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:10:02.7365889Z 2025-03-04T21:10:02.7366199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:02.7366298Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:10:02.7366424Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:10:02.7366488Z 2025-03-04T21:10:02.7366797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:02.7366894Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:10:02.7367014Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:10:02.7367077Z 2025-03-04T21:10:02.7367383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:02.7367498Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:10:02.7367637Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:10:02.7367700Z 2025-03-04T21:10:02.7368002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:02.7368115Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:10:02.7368253Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:10:02.7368315Z 2025-03-04T21:10:02.7368696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:02.7368893Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:10:02.7368981Z 2025-03-04T21:10:02.7369311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:02.7369502Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:10:02.7369568Z 2025-03-04T21:10:02.7369977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:02.7370162Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:10:02.7370232Z 2025-03-04T21:10:02.7370618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:02.7370834Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:10:02.7370898Z 2025-03-04T21:10:02.7371341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:02.7371503Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:10:02.7371656Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:10:02.7371804Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:10:02.7371870Z 2025-03-04T21:10:02.7372247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:02.7372417Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:10:02.7372488Z 2025-03-04T21:10:02.7372803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:02.7372959Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:10:02.7373024Z 2025-03-04T21:10:02.7373343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:02.7373474Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:10:02.7373609Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7373761Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:10:02.7373831Z 2025-03-04T21:10:02.7374151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:02.7374285Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:10:02.7374415Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:10:02.7374672Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:10:02.7374752Z 2025-03-04T21:10:02.7375132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:02.7375273Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7375407Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:10:02.7375558Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:10:02.7375639Z 2025-03-04T21:10:02.7376010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:02.7376217Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:10:02.7376317Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:10:02.7376462Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:10:02.7376530Z 2025-03-04T21:10:02.7376855Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:02.7377017Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:02.7377148Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:10:02.7377214Z 2025-03-04T21:10:02.7377530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:02.7377687Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:02.7377813Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:10:02.7377880Z 2025-03-04T21:10:02.7378197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:02.7378349Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:02.7378471Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:10:02.7378538Z 2025-03-04T21:10:02.7378864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:02.7379056Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:10:02.7379169Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:10:02.7379243Z 2025-03-04T21:10:02.7379590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:02.7379745Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:10:02.7379813Z 2025-03-04T21:10:02.7380160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:02.7380306Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:10:02.7380378Z 2025-03-04T21:10:02.7380732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:02.7380879Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:10:02.7381008Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:10:02.7381195Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:10:02.7381342Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:10:02.7381434Z 2025-03-04T21:10:02.7381790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:02.7381953Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:10:02.7382096Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:10:02.7382259Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:10:02.7382399Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:10:02.7382475Z 2025-03-04T21:10:02.7382815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:02.7382946Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:10:02.7383107Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:10:02.7383256Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:10:02.7383323Z 2025-03-04T21:10:02.7383670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:02.7383788Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:10:02.7383964Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:10:02.7384101Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:10:02.7384175Z 2025-03-04T21:10:02.7384496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:02.7384606Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:10:02.7384730Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:10:02.7384804Z 2025-03-04T21:10:02.7385123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:02.7385283Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:10:02.7385437Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:10:02.7385526Z 2025-03-04T21:10:02.7386034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:02.7386173Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:10:02.7386317Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:10:02.7386393Z 2025-03-04T21:10:02.7386707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:02.7386835Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:10:02.7386976Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:10:02.7387045Z 2025-03-04T21:10:02.7387580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:02.7387783Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:10:02.7387882Z 2025-03-04T21:10:02.7388535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:02.7388821Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:10:02.7388954Z 2025-03-04T21:10:02.7389398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:02.7389645Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:10:02.7389722Z 2025-03-04T21:10:02.7390208Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:02.7390436Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:10:02.7390503Z 2025-03-04T21:10:02.7391015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:02.7391177Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:10:02.7391341Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:10:02.7391533Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:10:02.7391627Z 2025-03-04T21:10:02.7392095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:02.7392375Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:10:02.7392469Z 2025-03-04T21:10:02.7392846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:02.7393072Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:10:02.7393172Z 2025-03-04T21:10:02.7393610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:02.7393809Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:10:02.7393999Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7394235Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:10:02.7394308Z 2025-03-04T21:10:02.7394655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:02.7394844Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:10:02.7394972Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:10:02.7395138Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:10:02.7395209Z 2025-03-04T21:10:02.7395710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:02.7395919Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7396060Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:10:02.7396250Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:10:02.7396368Z 2025-03-04T21:10:02.7396836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:02.7397082Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:10:02.7397212Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:10:02.7397414Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:10:02.7397482Z 2025-03-04T21:10:02.7397829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:02.7398067Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:02.7398236Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:10:02.7398317Z 2025-03-04T21:10:02.7398824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:02.7399059Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:02.7399239Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:10:02.7399324Z 2025-03-04T21:10:02.7399767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:02.7399925Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:02.7400049Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:10:02.7400116Z 2025-03-04T21:10:02.7400438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:02.7400625Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:10:02.7400752Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:10:02.7400819Z 2025-03-04T21:10:02.7401184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:02.7401333Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:10:02.7401420Z 2025-03-04T21:10:02.7401768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:02.7401930Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:10:02.7402006Z 2025-03-04T21:10:02.7402453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:02.7402595Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:10:02.7402760Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:10:02.7402930Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:10:02.7403096Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:10:02.7403172Z 2025-03-04T21:10:02.7403558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:02.7403711Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:10:02.7403857Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:10:02.7404026Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:10:02.7404172Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:10:02.7404247Z 2025-03-04T21:10:02.7404605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:02.7404788Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:10:02.7405011Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:10:02.7405225Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:10:02.7405320Z 2025-03-04T21:10:02.7405742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:02.7405867Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:10:02.7406057Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:10:02.7406196Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:10:02.7406271Z 2025-03-04T21:10:02.7406599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:02.7406712Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:10:02.7406837Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:10:02.7406911Z 2025-03-04T21:10:02.7407235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:02.7407341Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:10:02.7407465Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:10:02.7407539Z 2025-03-04T21:10:02.7407860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:02.7407989Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:10:02.7408130Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:10:02.7408207Z 2025-03-04T21:10:02.7408602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:02.7408734Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:10:02.7408870Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:10:02.7408950Z 2025-03-04T21:10:02.7409349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:02.7409640Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:10:02.7409711Z 2025-03-04T21:10:02.7410084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:02.7410280Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:10:02.7410359Z 2025-03-04T21:10:02.7410761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:02.7410958Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:10:02.7411028Z 2025-03-04T21:10:02.7411461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:02.7411691Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:10:02.7411762Z 2025-03-04T21:10:02.7412231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:02.7412393Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:10:02.7412563Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:10:02.7412709Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:10:02.7412789Z 2025-03-04T21:10:02.7413178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:02.7413365Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:10:02.7413437Z 2025-03-04T21:10:02.7413777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:02.7413932Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:10:02.7414013Z 2025-03-04T21:10:02.7414345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:02.7414554Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:10:02.7414698Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7414869Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:10:02.7414941Z 2025-03-04T21:10:02.7415304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:02.7415444Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:10:02.7415602Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:10:02.7415847Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:10:02.7415928Z 2025-03-04T21:10:02.7416399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:02.7416588Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7416685Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:10:02.7416859Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:10:02.7416928Z 2025-03-04T21:10:02.7417267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:02.7417441Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:10:02.7417560Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:10:02.7417746Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:10:02.7417847Z 2025-03-04T21:10:02.7418190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:02.7418358Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:02.7418477Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:10:02.7418551Z 2025-03-04T21:10:02.7418941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:02.7419106Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:02.7419230Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:10:02.7419298Z 2025-03-04T21:10:02.7419687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:02.7419841Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:02.7419961Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:10:02.7420030Z 2025-03-04T21:10:02.7420352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:02.7420542Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:10:02.7420662Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:10:02.7420729Z 2025-03-04T21:10:02.7421090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:02.7421233Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:10:02.7421306Z 2025-03-04T21:10:02.7421651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:02.7421794Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:10:02.7421861Z 2025-03-04T21:10:02.7422227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:02.7422367Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:10:02.7422517Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:10:02.7422676Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:10:02.7422858Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:10:02.7422924Z 2025-03-04T21:10:02.7423308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:02.7423472Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:10:02.7423608Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:10:02.7423762Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:10:02.7423910Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:10:02.7423975Z 2025-03-04T21:10:02.7424323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:02.7424443Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:10:02.7424616Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:10:02.7424752Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:10:02.7424828Z 2025-03-04T21:10:02.7425168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:02.7425291Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:10:02.7425465Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:10:02.7425606Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:10:02.7425676Z 2025-03-04T21:10:02.7426002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:02.7426113Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:10:02.7426231Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:10:02.7426307Z 2025-03-04T21:10:02.7426622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:02.7426727Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:10:02.7426845Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:10:02.7426921Z 2025-03-04T21:10:02.7427232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:02.7427360Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:10:02.7427496Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:10:02.7427573Z 2025-03-04T21:10:02.7427928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:02.7428054Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:10:02.7428185Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:10:02.7428260Z 2025-03-04T21:10:02.7428636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:02.7428854Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:10:02.7428921Z 2025-03-04T21:10:02.7429287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:02.7429468Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:10:02.7429542Z 2025-03-04T21:10:02.7429937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:02.7430124Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:10:02.7430192Z 2025-03-04T21:10:02.7430703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:10:02.7430845Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:10:02.7430918Z 2025-03-04T21:10:02.7431312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7431494Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:10:02.7431561Z 2025-03-04T21:10:02.7432018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:02.7432137Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:10:02.7432255Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:10:02.7432372Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:10:02.7432447Z 2025-03-04T21:10:02.7432925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:02.7433073Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:02.7433311Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:10:02.7433387Z 2025-03-04T21:10:02.7433862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:02.7434041Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:02.7434114Z 2025-03-04T21:10:02.7434428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7434560Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:10:02.7434626Z 2025-03-04T21:10:02.7435079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:02.7435201Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:10:02.7435318Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:10:02.7435455Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:10:02.7435528Z 2025-03-04T21:10:02.7436000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:02.7436156Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:02.7436388Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:10:02.7436460Z 2025-03-04T21:10:02.7436914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:02.7437088Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:02.7437152Z 2025-03-04T21:10:02.7437452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7437579Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:10:02.7437650Z 2025-03-04T21:10:02.7438076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:02.7438200Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:10:02.7438306Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:10:02.7438429Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:10:02.7438496Z 2025-03-04T21:10:02.7438959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:02.7439093Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:02.7439336Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:10:02.7439400Z 2025-03-04T21:10:02.7439856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:02.7440025Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:02.7440091Z 2025-03-04T21:10:02.7440389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7440513Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:10:02.7440585Z 2025-03-04T21:10:02.7441014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:02.7441136Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:10:02.7441288Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:10:02.7441413Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:10:02.7441503Z 2025-03-04T21:10:02.7441962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:02.7442110Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:02.7442351Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:10:02.7442431Z 2025-03-04T21:10:02.7442886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:02.7443050Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:02.7443124Z 2025-03-04T21:10:02.7443415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7443547Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:10:02.7443612Z 2025-03-04T21:10:02.7444045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:02.7444159Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:10:02.7444271Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:10:02.7444388Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:10:02.7444461Z 2025-03-04T21:10:02.7444910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:02.7445085Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:10:02.7445316Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:10:02.7445391Z 2025-03-04T21:10:02.7445835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:02.7446007Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:02.7446073Z 2025-03-04T21:10:02.7446371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7446500Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:10:02.7446565Z 2025-03-04T21:10:02.7446858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:02.7447238Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:10:02.7447308Z 2025-03-04T21:10:02.7448242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:02.7448721Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:10:02.7448803Z 2025-03-04T21:10:02.7449105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:02.7449327Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:10:02.7449399Z 2025-03-04T21:10:02.7449794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:10:02.7449949Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:10:02.7450016Z 2025-03-04T21:10:02.7450332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:10:02.7450488Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:10:02.7450563Z 2025-03-04T21:10:02.7450952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:10:02.7451100Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:10:02.7451166Z 2025-03-04T21:10:02.7451672Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:10:02.7451817Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:10:02.7451950Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:10:02.7452112Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:10:02.7452254Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:10:02.7452323Z 2025-03-04T21:10:02.7452709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:10:02.7452835Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:10:02.7452904Z 2025-03-04T21:10:02.7453449Z 2025-03-04T21:10:02.7453558Z class GraphModule(torch.nn.Module): 2025-03-04T21:10:02.7519507Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", 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L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 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L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:10:02.7520089Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:10:02.7520519Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.7521001Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7521460Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7521895Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7522357Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7522813Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.7523349Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7523835Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7524326Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7524789Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7525204Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.7525678Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7526151Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7526596Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7527068Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7527491Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.7527965Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7528437Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7528890Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7529341Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7529793Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:10:02.7530317Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7530826Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7531306Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7531804Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7532222Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.7532710Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7533184Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7533644Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7534099Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7534611Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.7535163Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7535672Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7536137Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7536621Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7537074Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.7537588Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7538148Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7538614Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7539108Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7539540Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.7540050Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7540534Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7541007Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7541464Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7541888Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.7542371Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7542867Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7543322Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7543782Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7544200Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.7544700Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7545175Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7545548Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7545942Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7546282Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.7546722Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7547135Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7547520Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7547897Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7548246Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.7548654Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7549052Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7549439Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7549816Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7550159Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.7550563Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7550968Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7551394Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7551805Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7552220Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:10:02.7552717Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7553204Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7553676Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7554150Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7554532Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.7554931Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7555334Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7555717Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7556083Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7556432Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.7556829Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7557230Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7557605Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7557979Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7558327Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.7558725Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7559143Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7559518Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7559930Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7560286Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.7560701Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7561106Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7561485Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7561864Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7562212Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.7562666Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7563092Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7563497Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7563920Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7564281Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.7564712Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7565152Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7565584Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7566017Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7566424Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.7566901Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7567358Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7567785Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7568197Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7568593Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.7569039Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7569485Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7569913Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7570305Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7570656Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.7571057Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7571463Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7571843Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7572239Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7572648Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.7573095Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7573574Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7573995Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7574431Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7574872Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.7575386Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7575925Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7576432Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7576929Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7577321Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.7577784Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7578235Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7578671Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7579099Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7579510Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:10:02.7579990Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7580490Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7580974Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7581432Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7581830Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.7582292Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7582739Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7583166Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7583577Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7583974Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.7584430Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7584878Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7585314Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7585730Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7586120Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.7586576Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7587029Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7587487Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7587894Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7588414Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.7588920Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7589410Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7589832Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7590260Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7590651Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.7591100Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7591557Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7591980Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7592406Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7592789Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.7593252Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7593696Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7594117Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7594539Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7594944Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.7595394Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7595872Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7596283Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7596661Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7596998Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.7597402Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7597792Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7598179Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7598544Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7598892Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.7599295Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7599689Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7600075Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7600444Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7600796Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.7601206Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7601667Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7602112Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7602540Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7602959Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.7603357Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7603761Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7604138Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7604515Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7604886Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.7605330Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7605777Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7606195Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7606614Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7606995Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.7607454Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7607897Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7608341Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7608752Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7609165Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.7609617Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7610086Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7610501Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7610921Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7611298Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.7611754Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7612214Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7612650Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7613078Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7613466Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.7613938Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7614447Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7615010Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7615497Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7615979Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.7616443Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7616935Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7617391Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7617812Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7618207Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.7618661Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7619120Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7619556Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7619970Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7620390Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:10:02.7620844Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7621311Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7621743Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7622175Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7622558Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.7623026Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7623473Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7623924Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7624339Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7624740Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.7625195Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7625645Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7626072Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7626497Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7626882Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.7627336Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7627776Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7628205Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7628625Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7629009Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.7629465Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7629908Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7630356Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7630767Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7631196Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.7631660Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7632100Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7632527Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7632937Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7633327Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.7633770Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.7634212Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.7634638Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.7635046Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.7635307Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T21:10:02.7635549Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T21:10:02.7635799Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T21:10:02.7636030Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T21:10:02.7636280Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T21:10:02.7636514Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T21:10:02.7636763Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T21:10:02.7636991Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T21:10:02.7637269Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T21:10:02.7637525Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T21:10:02.7637793Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T21:10:02.7638050Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T21:10:02.7638294Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T21:10:02.7638519Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T21:10:02.7638747Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T21:10:02.7638967Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T21:10:02.7639346Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:10:02.7639727Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:10:02.7640093Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:10:02.7640463Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:10:02.7640826Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:10:02.7641173Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:10:02.7641519Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:10:02.7641897Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:10:02.7642282Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:10:02.7642652Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:10:02.7643022Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:10:02.7643096Z 2025-03-04T21:10:02.7643417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7644046Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7644128Z 2025-03-04T21:10:02.7644425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7646305Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7646401Z 2025-03-04T21:10:02.7646703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:10:02.7646862Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:10:02.7646933Z 2025-03-04T21:10:02.7647321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:10:02.7647579Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:10:02.7647656Z 2025-03-04T21:10:02.7647928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7648467Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7648544Z 2025-03-04T21:10:02.7648828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7650717Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7650796Z 2025-03-04T21:10:02.7651099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7651278Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:10:02.7651348Z 2025-03-04T21:10:02.7651633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7652213Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7652318Z 2025-03-04T21:10:02.7652620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7654669Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7654759Z 2025-03-04T21:10:02.7655087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7655255Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:10:02.7655329Z 2025-03-04T21:10:02.7655628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7656210Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7656287Z 2025-03-04T21:10:02.7656570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7658491Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7658572Z 2025-03-04T21:10:02.7658867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7659413Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:10:02.7659527Z 2025-03-04T21:10:02.7659818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7661829Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7661903Z 2025-03-04T21:10:02.7662215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7662374Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:10:02.7662452Z 2025-03-04T21:10:02.7662760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7662928Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:10:02.7662996Z 2025-03-04T21:10:02.7663275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7663802Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7663872Z 2025-03-04T21:10:02.7664165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7666098Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7666194Z 2025-03-04T21:10:02.7666512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7666661Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:10:02.7666779Z 2025-03-04T21:10:02.7667038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7667591Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7667659Z 2025-03-04T21:10:02.7667948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7669763Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7669840Z 2025-03-04T21:10:02.7670135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7670281Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:10:02.7670353Z 2025-03-04T21:10:02.7670605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7671107Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7671174Z 2025-03-04T21:10:02.7671484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7673458Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7673548Z 2025-03-04T21:10:02.7673868Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7674039Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:10:02.7674132Z 2025-03-04T21:10:02.7674438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7674598Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:10:02.7674663Z 2025-03-04T21:10:02.7674930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7675448Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7675527Z 2025-03-04T21:10:02.7675809Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7677712Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7677794Z 2025-03-04T21:10:02.7678097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7678253Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:10:02.7678320Z 2025-03-04T21:10:02.7678596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7679128Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7679209Z 2025-03-04T21:10:02.7679482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7681310Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7681417Z 2025-03-04T21:10:02.7681712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7681851Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:10:02.7681925Z 2025-03-04T21:10:02.7682178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7682682Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7682748Z 2025-03-04T21:10:02.7683021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7684794Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7684868Z 2025-03-04T21:10:02.7685155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7685312Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:10:02.7685385Z 2025-03-04T21:10:02.7685670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7685832Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:10:02.7685897Z 2025-03-04T21:10:02.7686153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7686673Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7686764Z 2025-03-04T21:10:02.7687031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7689135Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7689248Z 2025-03-04T21:10:02.7689557Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7689723Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:10:02.7689791Z 2025-03-04T21:10:02.7690071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7690600Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7690679Z 2025-03-04T21:10:02.7690963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7692886Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7692967Z 2025-03-04T21:10:02.7693271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7693435Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:10:02.7693505Z 2025-03-04T21:10:02.7693810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7694340Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7694433Z 2025-03-04T21:10:02.7694864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7696881Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7696960Z 2025-03-04T21:10:02.7697244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7697782Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:10:02.7697862Z 2025-03-04T21:10:02.7698143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7700107Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7700188Z 2025-03-04T21:10:02.7700489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7700663Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:10:02.7700733Z 2025-03-04T21:10:02.7701042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7701223Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:10:02.7701300Z 2025-03-04T21:10:02.7701583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7702121Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7702206Z 2025-03-04T21:10:02.7702493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7704397Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7704467Z 2025-03-04T21:10:02.7704779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7704931Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:10:02.7705007Z 2025-03-04T21:10:02.7705271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7705797Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7705867Z 2025-03-04T21:10:02.7706155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7707941Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7708024Z 2025-03-04T21:10:02.7708321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7708480Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:10:02.7708551Z 2025-03-04T21:10:02.7708815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7709321Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7709416Z 2025-03-04T21:10:02.7709687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7711490Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7711567Z 2025-03-04T21:10:02.7711853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7712019Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:10:02.7712084Z 2025-03-04T21:10:02.7712379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7712531Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:10:02.7712604Z 2025-03-04T21:10:02.7712858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7713355Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7713421Z 2025-03-04T21:10:02.7713701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7715509Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7715596Z 2025-03-04T21:10:02.7715893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7716054Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:10:02.7716129Z 2025-03-04T21:10:02.7716382Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7716882Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7716948Z 2025-03-04T21:10:02.7717223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7719028Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7719095Z 2025-03-04T21:10:02.7719390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7719532Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:10:02.7719604Z 2025-03-04T21:10:02.7719856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7720363Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7720428Z 2025-03-04T21:10:02.7720700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7722600Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7722709Z 2025-03-04T21:10:02.7723017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7723177Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:10:02.7723250Z 2025-03-04T21:10:02.7723534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7723693Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:10:02.7723758Z 2025-03-04T21:10:02.7724018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7724516Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7724581Z 2025-03-04T21:10:02.7724853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7726687Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7726762Z 2025-03-04T21:10:02.7727057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7727203Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:10:02.7727275Z 2025-03-04T21:10:02.7727539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7728102Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7728172Z 2025-03-04T21:10:02.7728469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7730420Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7730514Z 2025-03-04T21:10:02.7730860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7731012Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:10:02.7731090Z 2025-03-04T21:10:02.7731357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7731904Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7731973Z 2025-03-04T21:10:02.7732264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7734170Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7734248Z 2025-03-04T21:10:02.7734683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7734869Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:10:02.7734952Z 2025-03-04T21:10:02.7735277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7735482Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:10:02.7735558Z 2025-03-04T21:10:02.7735852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7736399Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7736496Z 2025-03-04T21:10:02.7736787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7738671Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7738754Z 2025-03-04T21:10:02.7739060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7739217Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:10:02.7739288Z 2025-03-04T21:10:02.7739565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7740085Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7740154Z 2025-03-04T21:10:02.7740444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7742354Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7742432Z 2025-03-04T21:10:02.7742760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7742908Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:10:02.7743005Z 2025-03-04T21:10:02.7743271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7743821Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7743906Z 2025-03-04T21:10:02.7744198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7746038Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7746115Z 2025-03-04T21:10:02.7746376Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7746870Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:10:02.7746944Z 2025-03-04T21:10:02.7747206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7749029Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7749104Z 2025-03-04T21:10:02.7749380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7749544Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:10:02.7749608Z 2025-03-04T21:10:02.7749917Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7750059Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:10:02.7750131Z 2025-03-04T21:10:02.7750395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7750901Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7750967Z 2025-03-04T21:10:02.7751240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7753038Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7753106Z 2025-03-04T21:10:02.7753400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7753535Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:10:02.7753607Z 2025-03-04T21:10:02.7753859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7754351Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7754424Z 2025-03-04T21:10:02.7754689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7756487Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7756578Z 2025-03-04T21:10:02.7756866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7757028Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:10:02.7757109Z 2025-03-04T21:10:02.7757366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7757857Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7757930Z 2025-03-04T21:10:02.7758193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7760074Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7760153Z 2025-03-04T21:10:02.7760447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7760613Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:10:02.7760681Z 2025-03-04T21:10:02.7760997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7761141Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:10:02.7761212Z 2025-03-04T21:10:02.7761460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7761944Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7762010Z 2025-03-04T21:10:02.7762287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7764152Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7764264Z 2025-03-04T21:10:02.7764579Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7764723Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:10:02.7764804Z 2025-03-04T21:10:02.7765069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7765603Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7765675Z 2025-03-04T21:10:02.7765961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7767806Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7767877Z 2025-03-04T21:10:02.7768188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7768329Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:10:02.7768405Z 2025-03-04T21:10:02.7768672Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7769195Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7769270Z 2025-03-04T21:10:02.7769553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7771469Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7771579Z 2025-03-04T21:10:02.7771893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7772066Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:10:02.7772137Z 2025-03-04T21:10:02.7772460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7772619Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:10:02.7772698Z 2025-03-04T21:10:02.7772980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7773523Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7773598Z 2025-03-04T21:10:02.7773904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7775938Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7776037Z 2025-03-04T21:10:02.7776370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7776520Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:10:02.7776600Z 2025-03-04T21:10:02.7776882Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7777460Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7777594Z 2025-03-04T21:10:02.7777899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7779918Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7780010Z 2025-03-04T21:10:02.7780341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7780492Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:10:02.7780574Z 2025-03-04T21:10:02.7780859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7781398Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7781467Z 2025-03-04T21:10:02.7781757Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7783660Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7783731Z 2025-03-04T21:10:02.7784035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7784188Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:10:02.7784264Z 2025-03-04T21:10:02.7784580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7784757Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:10:02.7784824Z 2025-03-04T21:10:02.7785096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7785636Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7785721Z 2025-03-04T21:10:02.7786010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7787925Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7788007Z 2025-03-04T21:10:02.7788440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7788601Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:10:02.7788683Z 2025-03-04T21:10:02.7788974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7789528Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7789599Z 2025-03-04T21:10:02.7789899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7791972Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7792059Z 2025-03-04T21:10:02.7792409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7792558Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:10:02.7792636Z 2025-03-04T21:10:02.7792939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7793515Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7793590Z 2025-03-04T21:10:02.7793891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7795883Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7795953Z 2025-03-04T21:10:02.7796242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7796392Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:10:02.7796467Z 2025-03-04T21:10:02.7796751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7796900Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:10:02.7796964Z 2025-03-04T21:10:02.7797227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7797704Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7797779Z 2025-03-04T21:10:02.7798043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7799879Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7800001Z 2025-03-04T21:10:02.7800296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7800439Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:10:02.7800504Z 2025-03-04T21:10:02.7800764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7801258Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7801323Z 2025-03-04T21:10:02.7801594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7803412Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7803487Z 2025-03-04T21:10:02.7803780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7803914Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:10:02.7803986Z 2025-03-04T21:10:02.7804238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7804736Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7804803Z 2025-03-04T21:10:02.7805076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7806893Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7807013Z 2025-03-04T21:10:02.7807301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7807449Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:10:02.7807522Z 2025-03-04T21:10:02.7807805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7807953Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:10:02.7808017Z 2025-03-04T21:10:02.7808273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7808753Z x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7808826Z 2025-03-04T21:10:02.7809092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7810904Z x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7810980Z 2025-03-04T21:10:02.7811283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7811434Z out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:10:02.7811504Z 2025-03-04T21:10:02.7811774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7812298Z x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7812377Z 2025-03-04T21:10:02.7812673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7814654Z x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7814771Z 2025-03-04T21:10:02.7815111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7815275Z out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:10:02.7815352Z 2025-03-04T21:10:02.7815653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7816222Z x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7816294Z 2025-03-04T21:10:02.7816597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7818497Z x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7818577Z 2025-03-04T21:10:02.7818854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7819380Z x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:10:02.7819478Z 2025-03-04T21:10:02.7819766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7821831Z x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7821926Z 2025-03-04T21:10:02.7822222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7822379Z x_93 += x_95; out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93; x_93 = x_95 = None 2025-03-04T21:10:02.7822448Z 2025-03-04T21:10:02.7822760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7822911Z out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:10:02.7822986Z 2025-03-04T21:10:02.7823254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7823764Z x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7823835Z 2025-03-04T21:10:02.7824125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7826028Z x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7826100Z 2025-03-04T21:10:02.7826410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7826551Z out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:10:02.7826649Z 2025-03-04T21:10:02.7826914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7827461Z x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7827529Z 2025-03-04T21:10:02.7827814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7829714Z x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7829785Z 2025-03-04T21:10:02.7830096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7830237Z out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99); x_99 = None 2025-03-04T21:10:02.7830314Z 2025-03-04T21:10:02.7830579Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7831108Z x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7831186Z 2025-03-04T21:10:02.7831467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7833370Z x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7833444Z 2025-03-04T21:10:02.7833746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7833910Z x_101 += out_55; out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101; x_101 = out_55 = None 2025-03-04T21:10:02.7833989Z 2025-03-04T21:10:02.7834282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7834426Z out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:10:02.7834512Z 2025-03-04T21:10:02.7834767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7835275Z x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.7835340Z 2025-03-04T21:10:02.7835612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7837417Z x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7837493Z 2025-03-04T21:10:02.7837789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7837931Z out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:10:02.7838008Z 2025-03-04T21:10:02.7838262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7838762Z x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.7838830Z 2025-03-04T21:10:02.7839112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7840923Z x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7841007Z 2025-03-04T21:10:02.7841325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7841461Z out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105); x_105 = None 2025-03-04T21:10:02.7841548Z 2025-03-04T21:10:02.7841800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7842300Z x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.7842366Z 2025-03-04T21:10:02.7842651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.7844548Z x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.7844616Z 2025-03-04T21:10:02.7844911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.7845066Z x_107 += out_59; out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107; x_107 = out_59 = None 2025-03-04T21:10:02.7845138Z 2025-03-04T21:10:02.7845422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.7845572Z out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:10:02.7845637Z 2025-03-04T21:10:02.7845896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7846473Z x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T21:10:02.7846540Z 2025-03-04T21:10:02.7846799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7847358Z x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T21:10:02.7847443Z 2025-03-04T21:10:02.7847864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:10:02.7848145Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest'); x_108 = None 2025-03-04T21:10:02.7848227Z 2025-03-04T21:10:02.7848489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7849050Z x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T21:10:02.7849122Z 2025-03-04T21:10:02.7849471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:10:02.7849677Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features; x_110 = top_down_features = None 2025-03-04T21:10:02.7849750Z 2025-03-04T21:10:02.7849999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7850573Z x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T21:10:02.7850640Z 2025-03-04T21:10:02.7851049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:10:02.7851369Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T21:10:02.7851442Z 2025-03-04T21:10:02.7851691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7852273Z x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T21:10:02.7852340Z 2025-03-04T21:10:02.7852692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:10:02.7852904Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1; x_112 = top_down_features_1 = None 2025-03-04T21:10:02.7852975Z 2025-03-04T21:10:02.7853224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7853830Z x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T21:10:02.7853921Z 2025-03-04T21:10:02.7854366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:10:02.7854839Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T21:10:02.7854920Z 2025-03-04T21:10:02.7855226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7855885Z x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T21:10:02.7855968Z 2025-03-04T21:10:02.7856360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:10:02.7856614Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2; x_114 = top_down_features_2 = None 2025-03-04T21:10:02.7856687Z 2025-03-04T21:10:02.7856984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7857754Z x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T21:10:02.7857829Z 2025-03-04T21:10:02.7858268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T21:10:02.7858514Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T21:10:02.7858598Z 2025-03-04T21:10:02.7859118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7859307Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:10:02.7859385Z 2025-03-04T21:10:02.7859739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7859904Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:10:02.7859987Z 2025-03-04T21:10:02.7860491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7860675Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:10:02.7860772Z 2025-03-04T21:10:02.7861124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7861305Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:10:02.7861386Z 2025-03-04T21:10:02.7861839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:02.7862062Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:10:02.7862201Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:10:02.7862351Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:10:02.7862426Z 2025-03-04T21:10:02.7862808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:02.7862945Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:10:02.7863024Z 2025-03-04T21:10:02.7863374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:02.7863513Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:10:02.7863580Z 2025-03-04T21:10:02.7863993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:02.7864222Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:10:02.7864298Z 2025-03-04T21:10:02.7864741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:02.7864882Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:10:02.7865344Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:10:02.7865478Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:10:02.7865616Z x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:10:02.7865683Z 2025-03-04T21:10:02.7866146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7866306Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:10:02.7866381Z 2025-03-04T21:10:02.7866690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7866849Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:10:02.7866917Z 2025-03-04T21:10:02.7867379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7867551Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:10:02.7867625Z 2025-03-04T21:10:02.7867930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7868108Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:10:02.7868176Z 2025-03-04T21:10:02.7868590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:02.7868814Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:10:02.7868932Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:10:02.7869068Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:10:02.7869145Z 2025-03-04T21:10:02.7869492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:02.7869637Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:10:02.7869704Z 2025-03-04T21:10:02.7870059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:02.7870192Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:10:02.7870270Z 2025-03-04T21:10:02.7870676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:02.7870905Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:10:02.7870970Z 2025-03-04T21:10:02.7871389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:02.7871530Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:10:02.7871956Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:10:02.7872095Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:10:02.7872213Z x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:10:02.7872285Z 2025-03-04T21:10:02.7872719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7872875Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:10:02.7872940Z 2025-03-04T21:10:02.7873243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7873385Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:10:02.7873462Z 2025-03-04T21:10:02.7873891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7874063Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:10:02.7874130Z 2025-03-04T21:10:02.7874433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7874604Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:10:02.7874677Z 2025-03-04T21:10:02.7875069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:02.7875290Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:10:02.7875395Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:10:02.7875524Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:10:02.7875590Z 2025-03-04T21:10:02.7875927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:02.7876052Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:10:02.7876123Z 2025-03-04T21:10:02.7876448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:02.7876578Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:10:02.7876643Z 2025-03-04T21:10:02.7877030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:02.7877243Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:10:02.7877315Z 2025-03-04T21:10:02.7877723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:02.7877863Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:10:02.7878294Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:10:02.7878419Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:10:02.7878541Z x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T21:10:02.7878608Z 2025-03-04T21:10:02.7879044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7879189Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:10:02.7879262Z 2025-03-04T21:10:02.7879555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7879699Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:10:02.7879766Z 2025-03-04T21:10:02.7880220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7880364Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:10:02.7880436Z 2025-03-04T21:10:02.7880743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7880885Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:10:02.7880966Z 2025-03-04T21:10:02.7881346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:02.7881553Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:10:02.7881663Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:10:02.7881784Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:10:02.7881855Z 2025-03-04T21:10:02.7882182Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:02.7882314Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:10:02.7882378Z 2025-03-04T21:10:02.7882712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:02.7882832Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:10:02.7882906Z 2025-03-04T21:10:02.7883285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:02.7883506Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:10:02.7883570Z 2025-03-04T21:10:02.7883987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:02.7884122Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:10:02.7884537Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:10:02.7884669Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:10:02.7884784Z x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T21:10:02.7884856Z 2025-03-04T21:10:02.7885280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7885435Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:10:02.7885500Z 2025-03-04T21:10:02.7885800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7885938Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:10:02.7886008Z 2025-03-04T21:10:02.7886499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.7886650Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:10:02.7886729Z 2025-03-04T21:10:02.7887026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.7887173Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:10:02.7887245Z 2025-03-04T21:10:02.7887621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:02.7887836Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:10:02.7887938Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:10:02.7888237Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:10:02.7888316Z 2025-03-04T21:10:02.7888671Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:02.7888803Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:10:02.7888882Z 2025-03-04T21:10:02.7889222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:02.7889362Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:10:02.7889430Z 2025-03-04T21:10:02.7889838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:02.7890068Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:10:02.7890147Z 2025-03-04T21:10:02.7890563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:02.7890701Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:10:02.7891139Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:10:02.7891265Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:10:02.7891396Z x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T21:10:02.7891463Z 2025-03-04T21:10:02.7891783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:10:02.7891920Z tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32); x_116 = None 2025-03-04T21:10:02.7892065Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32); x_117 = None 2025-03-04T21:10:02.7892194Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32); x_118 = None 2025-03-04T21:10:02.7892328Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32); x_119 = None 2025-03-04T21:10:02.7892450Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32); x_120 = None 2025-03-04T21:10:02.7892543Z 2025-03-04T21:10:02.7892862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7893393Z x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_115 = None 2025-03-04T21:10:02.7893484Z 2025-03-04T21:10:02.7893799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:02.7894029Z x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False); x_121 = None 2025-03-04T21:10:02.7894107Z 2025-03-04T21:10:02.7894539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:02.7895095Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:02.7895164Z 2025-03-04T21:10:02.7895542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:02.7896107Z x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_122 = None 2025-03-04T21:10:02.7896177Z 2025-03-04T21:10:02.7896456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7896969Z x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_113 = None 2025-03-04T21:10:02.7897045Z 2025-03-04T21:10:02.7897340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:02.7897549Z x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False); x_123 = None 2025-03-04T21:10:02.7897615Z 2025-03-04T21:10:02.7898005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:02.7898542Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:02.7898621Z 2025-03-04T21:10:02.7898990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:02.7899548Z x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_124 = None 2025-03-04T21:10:02.7899628Z 2025-03-04T21:10:02.7899920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7900429Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_111 = None 2025-03-04T21:10:02.7900515Z 2025-03-04T21:10:02.7900830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:02.7901052Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False); x_125 = None 2025-03-04T21:10:02.7901138Z 2025-03-04T21:10:02.7901523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:02.7902048Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:02.7902116Z 2025-03-04T21:10:02.7902493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:02.7903008Z x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_126 = None 2025-03-04T21:10:02.7903083Z 2025-03-04T21:10:02.7903345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7903836Z x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_109 = None 2025-03-04T21:10:02.7903910Z 2025-03-04T21:10:02.7904192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:02.7904386Z x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False); x_127 = None 2025-03-04T21:10:02.7904452Z 2025-03-04T21:10:02.7904846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:02.7905356Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:02.7905430Z 2025-03-04T21:10:02.7905796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:02.7906332Z x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_128 = None 2025-03-04T21:10:02.7906425Z 2025-03-04T21:10:02.7906719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.7907555Z x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:10:02.7907642Z 2025-03-04T21:10:02.7907944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:02.7908127Z x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False); x_129 = None 2025-03-04T21:10:02.7908204Z 2025-03-04T21:10:02.7908596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:02.7909551Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:10:02.7909619Z 2025-03-04T21:10:02.7909999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:02.7910845Z x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:10:02.7910912Z 2025-03-04T21:10:02.7911276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:10:02.7911446Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:10:02.7911604Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:10:02.7911774Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:10:02.7911932Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:10:02.7912091Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:10:02.7912244Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:10:02.7912396Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:10:02.7912544Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:10:02.7912695Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:10:02.7912855Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:10:02.7912922Z 2025-03-04T21:10:02.7913367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:10:02.7913567Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304); x_131 = None 2025-03-04T21:10:02.7913784Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:10:02.7913993Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:10:02.7914172Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152); x_132 = None 2025-03-04T21:10:02.7914354Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:10:02.7914540Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:10:02.7914703Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76); x_133 = None 2025-03-04T21:10:02.7936507Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:10:02.7936900Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:10:02.7937087Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38); x_134 = None 2025-03-04T21:10:02.7937264Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:10:02.7937458Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:10:02.7937608Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19); x_135 = None 2025-03-04T21:10:02.7937790Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:10:02.7937961Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:10:02.7938047Z 2025-03-04T21:10:02.7938508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:02.7938734Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:10:02.7938806Z 2025-03-04T21:10:02.7939275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:02.7939441Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:10:02.7939606Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:10:02.7939755Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:10:02.7939836Z 2025-03-04T21:10:02.7940223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:02.7940412Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:10:02.7940482Z 2025-03-04T21:10:02.7940909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:02.7941065Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:10:02.7941177Z 2025-03-04T21:10:02.7941506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:02.7941686Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:10:02.7941831Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7942021Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:10:02.7942096Z 2025-03-04T21:10:02.7942430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:02.7942571Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:10:02.7942699Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:10:02.7942871Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:10:02.7942939Z 2025-03-04T21:10:02.7943273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:02.7943403Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7943505Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:10:02.7943641Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:10:02.7943716Z 2025-03-04T21:10:02.7944043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:02.7944206Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:10:02.7944302Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:10:02.7944446Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:10:02.7944513Z 2025-03-04T21:10:02.7944873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:02.7945040Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:02.7945170Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:10:02.7945237Z 2025-03-04T21:10:02.7945558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:02.7945721Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:02.7945851Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:10:02.7945917Z 2025-03-04T21:10:02.7946232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:02.7946390Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:02.7946515Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:10:02.7946581Z 2025-03-04T21:10:02.7946902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:02.7947121Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:10:02.7947247Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:10:02.7947328Z 2025-03-04T21:10:02.7947681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:02.7947849Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:10:02.7947940Z 2025-03-04T21:10:02.7948279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:02.7948427Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:10:02.7948492Z 2025-03-04T21:10:02.7948849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:02.7948990Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:10:02.7949124Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:10:02.7949280Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:10:02.7949429Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:10:02.7949494Z 2025-03-04T21:10:02.7949851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:02.7950000Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:10:02.7950125Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:10:02.7950286Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:10:02.7950423Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:10:02.7950494Z 2025-03-04T21:10:02.7950829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:02.7950959Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:10:02.7951122Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:10:02.7951265Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:10:02.7951331Z 2025-03-04T21:10:02.7951674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:02.7951794Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:10:02.7951970Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:10:02.7952109Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:10:02.7952182Z 2025-03-04T21:10:02.7952499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:02.7952608Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:10:02.7952731Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:10:02.7952820Z 2025-03-04T21:10:02.7953134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:02.7953255Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:10:02.7953370Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:10:02.7953442Z 2025-03-04T21:10:02.7953767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:02.7953907Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:10:02.7954039Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:10:02.7954111Z 2025-03-04T21:10:02.7954415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:02.7954535Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:10:02.7954665Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:10:02.7954738Z 2025-03-04T21:10:02.7955086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:02.7955278Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:10:02.7955346Z 2025-03-04T21:10:02.7955687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:02.7955852Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:10:02.7955925Z 2025-03-04T21:10:02.7956308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:02.7956494Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:10:02.7956559Z 2025-03-04T21:10:02.7956968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:02.7957184Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:10:02.7957260Z 2025-03-04T21:10:02.7957691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:02.7957855Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:10:02.7958018Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:10:02.7958159Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:10:02.7958234Z 2025-03-04T21:10:02.7958606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:02.7958789Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:10:02.7958856Z 2025-03-04T21:10:02.7959195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:02.7959347Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:10:02.7959453Z 2025-03-04T21:10:02.7959767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:02.7959927Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:10:02.7960060Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7960241Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:10:02.7960307Z 2025-03-04T21:10:02.7960632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:02.7960758Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:10:02.7960893Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:10:02.7961049Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:10:02.7961121Z 2025-03-04T21:10:02.7961430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:02.7961562Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7961656Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:10:02.7961794Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:10:02.7961859Z 2025-03-04T21:10:02.7962177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:02.7962327Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:10:02.7962429Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:10:02.7962562Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:10:02.7962634Z 2025-03-04T21:10:02.7962935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:02.7963100Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:02.7963216Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:10:02.7963285Z 2025-03-04T21:10:02.7963584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:02.7963743Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:02.7963860Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:10:02.7963933Z 2025-03-04T21:10:02.7964229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:02.7964387Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:02.7964500Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:10:02.7964570Z 2025-03-04T21:10:02.7964893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:02.7965082Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:10:02.7965215Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:10:02.7965281Z 2025-03-04T21:10:02.7965636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:02.7965780Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:10:02.7965868Z 2025-03-04T21:10:02.7966199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:02.7966346Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:10:02.7966413Z 2025-03-04T21:10:02.7966765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:02.7966905Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:10:02.7967040Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:10:02.7967201Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:10:02.7967353Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:10:02.7967417Z 2025-03-04T21:10:02.7967768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:02.7967909Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:10:02.7968039Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:10:02.7968195Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:10:02.7968340Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:10:02.7968402Z 2025-03-04T21:10:02.7968738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:02.7968855Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:10:02.7969020Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:10:02.7969169Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:10:02.7969241Z 2025-03-04T21:10:02.7969574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:02.7969696Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:10:02.7969866Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:10:02.7970007Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:10:02.7970073Z 2025-03-04T21:10:02.7970394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:02.7970495Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:10:02.7970657Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:10:02.7970724Z 2025-03-04T21:10:02.7971044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:02.7971159Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:10:02.7971286Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:10:02.7971369Z 2025-03-04T21:10:02.7971696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:02.7971839Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:10:02.7971988Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:10:02.7972052Z 2025-03-04T21:10:02.7972374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:02.7972495Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:10:02.7972638Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:10:02.7972706Z 2025-03-04T21:10:02.7973076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:02.7973288Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:10:02.7973354Z 2025-03-04T21:10:02.7973710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:02.7973882Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:10:02.7973958Z 2025-03-04T21:10:02.7974351Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:02.7974646Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:10:02.7974727Z 2025-03-04T21:10:02.7975187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:02.7975424Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:10:02.7975503Z 2025-03-04T21:10:02.7976003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:02.7976171Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:10:02.7976328Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:10:02.7976480Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:10:02.7976549Z 2025-03-04T21:10:02.7976937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:02.7977131Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:10:02.7977208Z 2025-03-04T21:10:02.7977527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:02.7977703Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:10:02.7977771Z 2025-03-04T21:10:02.7978121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:02.7978261Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:10:02.7978420Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7978575Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:10:02.7978651Z 2025-03-04T21:10:02.7978983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:02.7979126Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:10:02.7979257Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:10:02.7979424Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:10:02.7979496Z 2025-03-04T21:10:02.7979836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:02.7979966Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7980070Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:10:02.7980207Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:10:02.7980284Z 2025-03-04T21:10:02.7980602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:02.7980764Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:10:02.7980871Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:10:02.7981009Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:10:02.7981086Z 2025-03-04T21:10:02.7981398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:02.7981568Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:02.7981689Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:10:02.7981768Z 2025-03-04T21:10:02.7982076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:02.7982244Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:02.7982363Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:10:02.7982438Z 2025-03-04T21:10:02.7982745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:02.7982909Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:02.7983025Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:10:02.7983100Z 2025-03-04T21:10:02.7983436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:02.7983637Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:10:02.7983775Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:10:02.7983848Z 2025-03-04T21:10:02.7984210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:02.7984385Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:10:02.7984451Z 2025-03-04T21:10:02.7984802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:02.7984943Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:10:02.7985018Z 2025-03-04T21:10:02.7985372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:02.7985519Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:10:02.7985647Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:10:02.7985813Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:10:02.7985960Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:10:02.7986034Z 2025-03-04T21:10:02.7986385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:02.7986561Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:10:02.7986695Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:10:02.7986850Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:10:02.7987001Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:10:02.7987068Z 2025-03-04T21:10:02.7987416Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:02.7987535Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:10:02.7987708Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:10:02.7987847Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:10:02.7987923Z 2025-03-04T21:10:02.7988412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:02.7988547Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:10:02.7988724Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:10:02.7988870Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:10:02.7988938Z 2025-03-04T21:10:02.7989265Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:02.7989436Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:10:02.7989567Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:10:02.7989666Z 2025-03-04T21:10:02.7989992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:02.7990094Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:10:02.7990244Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:10:02.7990310Z 2025-03-04T21:10:02.7990661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:02.7990782Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:10:02.7990931Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:10:02.7990997Z 2025-03-04T21:10:02.7991315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:02.7991435Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:10:02.7991575Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:10:02.7991641Z 2025-03-04T21:10:02.7992005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:02.7992202Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:10:02.7992277Z 2025-03-04T21:10:02.7992620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:02.7992795Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:10:02.7992862Z 2025-03-04T21:10:02.7993258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:02.7993438Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:10:02.7993512Z 2025-03-04T21:10:02.7993926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:02.7994140Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:10:02.7994205Z 2025-03-04T21:10:02.7994644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:02.7994802Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:10:02.7994954Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:10:02.7995099Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:10:02.7995167Z 2025-03-04T21:10:02.7995546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:02.7995732Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:10:02.7995805Z 2025-03-04T21:10:02.7996118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:02.7996304Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:10:02.7996369Z 2025-03-04T21:10:02.7996707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:02.7996857Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:10:02.7996991Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7997142Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:10:02.7997216Z 2025-03-04T21:10:02.7997534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:02.7997666Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:10:02.7997786Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:10:02.7997947Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:10:02.7998011Z 2025-03-04T21:10:02.7998325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:02.7998447Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:10:02.7998547Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:10:02.7998682Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:10:02.7998754Z 2025-03-04T21:10:02.7999062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:02.7999219Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:10:02.7999315Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:10:02.7999451Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:10:02.7999519Z 2025-03-04T21:10:02.7999834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:02.7999986Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:02.8000112Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:10:02.8000176Z 2025-03-04T21:10:02.8000481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:02.8000633Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:02.8000756Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:10:02.8000820Z 2025-03-04T21:10:02.8001121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:02.8001280Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:02.8001391Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:10:02.8001479Z 2025-03-04T21:10:02.8001783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:02.8001990Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:10:02.8002101Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:10:02.8002172Z 2025-03-04T21:10:02.8002825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:02.8003002Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:10:02.8003067Z 2025-03-04T21:10:02.8003411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:02.8003553Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:10:02.8003627Z 2025-03-04T21:10:02.8003968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:02.8004116Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:10:02.8004242Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:10:02.8004404Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:10:02.8004546Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:10:02.8004620Z 2025-03-04T21:10:02.8004964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:02.8005112Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:10:02.8005237Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:10:02.8005402Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:10:02.8005540Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:10:02.8005615Z 2025-03-04T21:10:02.8005949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:02.8006073Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:10:02.8006235Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:10:02.8006378Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:10:02.8006443Z 2025-03-04T21:10:02.8006785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:02.8006899Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:10:02.8007072Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:10:02.8007205Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:10:02.8007277Z 2025-03-04T21:10:02.8007603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:02.8007709Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:10:02.8007827Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:10:02.8007916Z 2025-03-04T21:10:02.8008224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:02.8008344Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:10:02.8008463Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:10:02.8008551Z 2025-03-04T21:10:02.8008860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:02.8008984Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:10:02.8009123Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:10:02.8009188Z 2025-03-04T21:10:02.8009494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:02.8009611Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:10:02.8009750Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:10:02.8009816Z 2025-03-04T21:10:02.8010168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:02.8010360Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:10:02.8010431Z 2025-03-04T21:10:02.8010767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:02.8010938Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:10:02.8011003Z 2025-03-04T21:10:02.8011391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:02.8011565Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:10:02.8011638Z 2025-03-04T21:10:02.8012033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:02.8012250Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:10:02.8012316Z 2025-03-04T21:10:02.8012766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:02.8012921Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:10:02.8013084Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:10:02.8013233Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:10:02.8013308Z 2025-03-04T21:10:02.8013695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:02.8013872Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:10:02.8013954Z 2025-03-04T21:10:02.8014275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:02.8014429Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:10:02.8014598Z 2025-03-04T21:10:02.8014960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:02.8015131Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:10:02.8015269Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:10:02.8015445Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:10:02.8015520Z 2025-03-04T21:10:02.8015870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:02.8015998Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:10:02.8016131Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:10:02.8016308Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:10:02.8016381Z 2025-03-04T21:10:02.8016735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:02.8016872Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:10:02.8016983Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:10:02.8017129Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:10:02.8017208Z 2025-03-04T21:10:02.8017556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:02.8017728Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:10:02.8017831Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:10:02.8017982Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:10:02.8018057Z 2025-03-04T21:10:02.8018402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:02.8018575Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:02.8018711Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:10:02.8018784Z 2025-03-04T21:10:02.8019125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:02.8019291Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:02.8019423Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:10:02.8019495Z 2025-03-04T21:10:02.8019833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:02.8019997Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:02.8020147Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:10:02.8020221Z 2025-03-04T21:10:02.8020567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:02.8020789Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:10:02.8020918Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:10:02.8021053Z 2025-03-04T21:10:02.8021443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:02.8021621Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:10:02.8021702Z 2025-03-04T21:10:02.8022080Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:02.8022237Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:10:02.8022311Z 2025-03-04T21:10:02.8022681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:02.8022815Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:10:02.8022944Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:10:02.8023097Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:10:02.8023242Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:10:02.8023305Z 2025-03-04T21:10:02.8023660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:02.8023801Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:10:02.8023921Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:10:02.8024080Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:10:02.8024219Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:10:02.8024292Z 2025-03-04T21:10:02.8024624Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:02.8024746Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:10:02.8024907Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:10:02.8025044Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:10:02.8025110Z 2025-03-04T21:10:02.8025453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:02.8025568Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:10:02.8025742Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:10:02.8025871Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:10:02.8025942Z 2025-03-04T21:10:02.8026272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:02.8026379Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:10:02.8026514Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:10:02.8026586Z 2025-03-04T21:10:02.8026892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:02.8027019Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:10:02.8027135Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:10:02.8027226Z 2025-03-04T21:10:02.8027535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:02.8027656Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:10:02.8027790Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:10:02.8027862Z 2025-03-04T21:10:02.8028166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:02.8028284Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:10:02.8028414Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:10:02.8028488Z 2025-03-04T21:10:02.8028835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:02.8029031Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:10:02.8029097Z 2025-03-04T21:10:02.8029439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:02.8029600Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:10:02.8029681Z 2025-03-04T21:10:02.8030057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:02.8030236Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:10:02.8030303Z 2025-03-04T21:10:02.8030792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:10:02.8030931Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:10:02.8031006Z 2025-03-04T21:10:02.8031302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8031452Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:10:02.8031522Z 2025-03-04T21:10:02.8031956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:02.8032080Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:10:02.8032187Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:10:02.8032330Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:10:02.8032397Z 2025-03-04T21:10:02.8032865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:02.8033016Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:02.8033270Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:10:02.8033352Z 2025-03-04T21:10:02.8033812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:02.8033980Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:02.8034054Z 2025-03-04T21:10:02.8034348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8034479Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:10:02.8034544Z 2025-03-04T21:10:02.8034979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:02.8035098Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:10:02.8035213Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:10:02.8035331Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:10:02.8035402Z 2025-03-04T21:10:02.8035857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:02.8036000Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:02.8036233Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:10:02.8036309Z 2025-03-04T21:10:02.8036758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:02.8036933Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:02.8036996Z 2025-03-04T21:10:02.8037293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8037420Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:10:02.8037490Z 2025-03-04T21:10:02.8037914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:02.8038038Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:10:02.8038152Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:10:02.8038266Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:10:02.8038336Z 2025-03-04T21:10:02.8038800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:02.8038942Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:02.8039197Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:10:02.8039268Z 2025-03-04T21:10:02.8039740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:02.8039928Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:02.8039992Z 2025-03-04T21:10:02.8040298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8040424Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:10:02.8040497Z 2025-03-04T21:10:02.8040920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:02.8041045Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:10:02.8041151Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:10:02.8041279Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:10:02.8041342Z 2025-03-04T21:10:02.8041800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:02.8041934Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:02.8042178Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:10:02.8042244Z 2025-03-04T21:10:02.8042700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:02.8042867Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:02.8042942Z 2025-03-04T21:10:02.8043233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8043365Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:10:02.8043429Z 2025-03-04T21:10:02.8043862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:02.8043987Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:10:02.8044095Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:10:02.8044221Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:10:02.8044288Z 2025-03-04T21:10:02.8044743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:02.8044928Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:10:02.8045169Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:10:02.8045249Z 2025-03-04T21:10:02.8045731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:02.8045896Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:02.8045986Z 2025-03-04T21:10:02.8046280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8046409Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:10:02.8046474Z 2025-03-04T21:10:02.8046761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:02.8047143Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:10:02.8047218Z 2025-03-04T21:10:02.8047497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:02.8047967Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:10:02.8048032Z 2025-03-04T21:10:02.8048316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:02.8048519Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:10:02.8048591Z 2025-03-04T21:10:02.8048974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:10:02.8049126Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:10:02.8049190Z 2025-03-04T21:10:02.8049496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:10:02.8049657Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:10:02.8049722Z 2025-03-04T21:10:02.8050110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:10:02.8050245Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:10:02.8050320Z 2025-03-04T21:10:02.8050801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:10:02.8050948Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:10:02.8051073Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:10:02.8051253Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:10:02.8051389Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:10:02.8051486Z 2025-03-04T21:10:02.8051868Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:10:02.8052014Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:10:02.8052082Z 2025-03-04T21:10:02.8052111Z 2025-03-04T21:10:02.8052215Z class GraphModule(torch.nn.Module): 2025-03-04T21:10:02.8117712Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", 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"f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: 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L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 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1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:10:02.8180684Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:10:02.8181450Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.8182576Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8183563Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8184489Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8185402Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8186312Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.8187273Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8188376Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8189359Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8190280Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8191184Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.8192093Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8193002Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8193899Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8194817Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8195654Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.8196566Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8197510Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8198426Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8199282Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8200088Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:10:02.8200932Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8201834Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8202710Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8203563Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8204367Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.8205188Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8206062Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8206905Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8207726Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8208516Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.8209412Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8210357Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8211293Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8212185Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8213021Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.8213888Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8214870Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8215853Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8216747Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8217578Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.8218442Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8219365Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8220257Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8221129Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8221968Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.8222832Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8223790Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8224638Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8225493Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8226294Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.8227128Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8228007Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8228860Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8229692Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8230481Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.8231303Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8232174Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8233024Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8233849Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8234639Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.8235461Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8236342Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8237196Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8238049Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8238839Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.8239691Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8240581Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8241433Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8242251Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8243054Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:10:02.8243896Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8244794Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8245679Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8246532Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8247335Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.8248163Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8249038Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8249899Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8250737Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8251535Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.8252465Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8253476Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8254416Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8255469Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8256384Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.8257274Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8258224Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8259152Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8260046Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8260898Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.8261790Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8262739Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8263670Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8264558Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8265394Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.8266267Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8267230Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8268145Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8269063Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8269891Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.8270713Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8271626Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8272522Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8273341Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8274122Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.8274938Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8275814Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8276663Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8277481Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8278307Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.8279156Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8280021Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8280864Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8281702Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8282510Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.8283345Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8284224Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8285071Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8285892Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8286677Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.8287507Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8288586Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8289521Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8290448Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8291318Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.8292249Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8293233Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8294188Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8295235Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8296220Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.8297155Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8298186Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8299152Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8300119Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8301051Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:10:02.8302009Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8302977Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8303867Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8304744Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8305544Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.8306363Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8307242Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8308087Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8308907Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8309711Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.8310537Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8311427Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8312285Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8313101Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8313890Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.8314705Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8315561Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8316401Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8317218Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8317996Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.8318805Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8319669Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8320503Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8321310Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8322086Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.8322901Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8323767Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8324634Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8325431Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8326216Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.8327035Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8327912Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8328758Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8329578Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8330362Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.8331183Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8332057Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8332907Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8333722Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8334549Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.8335414Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8336385Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8337297Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8338143Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8338975Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.8339860Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8340822Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8341696Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8342524Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8343314Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.8344153Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8345040Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8345900Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8346744Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8347550Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.8348380Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8349262Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8350119Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8350950Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8351746Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.8352576Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8353476Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8354335Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8355164Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8355971Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.8356798Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8357674Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8358526Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8359353Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8360143Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.8360963Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8361840Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8362693Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8363522Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8364310Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.8365134Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8366006Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8366877Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8367699Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8368519Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.8369336Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8370240Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8371092Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8371912Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8372696Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.8373553Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8374534Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8375466Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8376410Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8377269Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.8378152Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8379097Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8380013Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8380899Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8381739Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:10:02.8382619Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8383556Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8384447Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8385298Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8386102Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.8386919Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8387796Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8388882Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8389777Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8390572Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.8391395Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8392274Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8393122Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8393951Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8394750Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.8395662Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8396533Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8397439Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8398254Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8399079Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:10:02.8399913Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8400791Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8401648Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8402473Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8403262Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:10:02.8404088Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8404968Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8405820Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8406649Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8407441Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:10:02.8408264Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:10:02.8409139Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:10:02.8410014Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:10:02.8410833Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:10:02.8411534Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T21:10:02.8412045Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T21:10:02.8412574Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T21:10:02.8413067Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T21:10:02.8413562Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T21:10:02.8414073Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T21:10:02.8414664Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T21:10:02.8415228Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T21:10:02.8415795Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T21:10:02.8416322Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T21:10:02.8416875Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T21:10:02.8417424Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T21:10:02.8417982Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T21:10:02.8418544Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T21:10:02.8419105Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T21:10:02.8419658Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T21:10:02.8420390Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:10:02.8421276Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:10:02.8422154Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:10:02.8423019Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:10:02.8423890Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:10:02.8424751Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:10:02.8425538Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:10:02.8426399Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:10:02.8427324Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:10:02.8428240Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:10:02.8429112Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:10:02.8429628Z 2025-03-04T21:10:02.8430043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8430988Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.8431678Z 2025-03-04T21:10:02.8432072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8434285Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8436220Z 2025-03-04T21:10:02.8436626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:10:02.8437131Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:10:02.8437412Z 2025-03-04T21:10:02.8437890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:10:02.8438580Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:10:02.8438959Z 2025-03-04T21:10:02.8439327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8440187Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.8440789Z 2025-03-04T21:10:02.8441167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8443440Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8445467Z 2025-03-04T21:10:02.8445868Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8446357Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:10:02.8446621Z 2025-03-04T21:10:02.8446966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8447773Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.8448380Z 2025-03-04T21:10:02.8448737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8450951Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8452947Z 2025-03-04T21:10:02.8453344Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8453865Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:10:02.8454143Z 2025-03-04T21:10:02.8454560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8455524Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.8456260Z 2025-03-04T21:10:02.8456685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8459098Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8461300Z 2025-03-04T21:10:02.8461694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8462643Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:10:02.8463360Z 2025-03-04T21:10:02.8463769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8466181Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8468412Z 2025-03-04T21:10:02.8468809Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.8469322Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:10:02.8469603Z 2025-03-04T21:10:02.8469999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8470541Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:10:02.8470830Z 2025-03-04T21:10:02.8471193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8472077Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.8472711Z 2025-03-04T21:10:02.8473082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8475382Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8477381Z 2025-03-04T21:10:02.8477775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8478301Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:10:02.8478586Z 2025-03-04T21:10:02.8478947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8479776Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.8480383Z 2025-03-04T21:10:02.8480741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8482854Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8484746Z 2025-03-04T21:10:02.8485145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8485649Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:10:02.8485960Z 2025-03-04T21:10:02.8486313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8487191Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.8487860Z 2025-03-04T21:10:02.8488395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8490697Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8492782Z 2025-03-04T21:10:02.8493202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.8493760Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:10:02.8494070Z 2025-03-04T21:10:02.8494532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8495105Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:10:02.8495421Z 2025-03-04T21:10:02.8495814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8496736Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.8497413Z 2025-03-04T21:10:02.8497808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8500220Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8502396Z 2025-03-04T21:10:02.8502845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8503422Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:10:02.8503721Z 2025-03-04T21:10:02.8504105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8505010Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.8505691Z 2025-03-04T21:10:02.8506085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8508469Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8510582Z 2025-03-04T21:10:02.8510975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8511489Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:10:02.8511769Z 2025-03-04T21:10:02.8512132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8512989Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.8513647Z 2025-03-04T21:10:02.8514026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8516337Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8518338Z 2025-03-04T21:10:02.8518710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.8519202Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:10:02.8519481Z 2025-03-04T21:10:02.8519854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8520349Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:10:02.8520630Z 2025-03-04T21:10:02.8520972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8521782Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.8522390Z 2025-03-04T21:10:02.8522745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8524901Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8526806Z 2025-03-04T21:10:02.8527180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8527677Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:10:02.8527944Z 2025-03-04T21:10:02.8528287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8529103Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.8529723Z 2025-03-04T21:10:02.8530102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8532344Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8534374Z 2025-03-04T21:10:02.8534828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8535363Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:10:02.8535661Z 2025-03-04T21:10:02.8536057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8536998Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.8537687Z 2025-03-04T21:10:02.8538061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8540309Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8542339Z 2025-03-04T21:10:02.8542699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8543560Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:10:02.8544216Z 2025-03-04T21:10:02.8544586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8546979Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8549239Z 2025-03-04T21:10:02.8549614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.8550113Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:10:02.8550384Z 2025-03-04T21:10:02.8550760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8551260Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:10:02.8551541Z 2025-03-04T21:10:02.8551889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8552707Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.8553317Z 2025-03-04T21:10:02.8553678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8555814Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8557718Z 2025-03-04T21:10:02.8558097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8558590Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:10:02.8558856Z 2025-03-04T21:10:02.8559203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8560037Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.8560665Z 2025-03-04T21:10:02.8561035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8563154Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8565063Z 2025-03-04T21:10:02.8565443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8565936Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:10:02.8566200Z 2025-03-04T21:10:02.8566546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8567362Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.8567979Z 2025-03-04T21:10:02.8568339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8570454Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8572341Z 2025-03-04T21:10:02.8572712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.8573227Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:10:02.8573509Z 2025-03-04T21:10:02.8573878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8574392Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:10:02.8574721Z 2025-03-04T21:10:02.8575083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8575994Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.8576710Z 2025-03-04T21:10:02.8577109Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8579463Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8581562Z 2025-03-04T21:10:02.8581989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8582554Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:10:02.8582851Z 2025-03-04T21:10:02.8583237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8584151Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.8584830Z 2025-03-04T21:10:02.8585231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8587563Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8589753Z 2025-03-04T21:10:02.8590154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8590720Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:10:02.8591008Z 2025-03-04T21:10:02.8591392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8592216Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.8592838Z 2025-03-04T21:10:02.8593200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8595360Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8597372Z 2025-03-04T21:10:02.8597770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.8598271Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:10:02.8598555Z 2025-03-04T21:10:02.8598929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8599434Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:10:02.8599702Z 2025-03-04T21:10:02.8600050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8600875Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.8601504Z 2025-03-04T21:10:02.8601776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8603668Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8603778Z 2025-03-04T21:10:02.8604077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8604227Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:10:02.8604290Z 2025-03-04T21:10:02.8604542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8605040Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.8605112Z 2025-03-04T21:10:02.8605379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8607154Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8607229Z 2025-03-04T21:10:02.8607515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8607664Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:10:02.8607730Z 2025-03-04T21:10:02.8607990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8608490Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.8608562Z 2025-03-04T21:10:02.8608825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8610627Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8610830Z 2025-03-04T21:10:02.8611108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.8611267Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:10:02.8611332Z 2025-03-04T21:10:02.8611622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8611771Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:10:02.8611844Z 2025-03-04T21:10:02.8612095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8612587Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.8612653Z 2025-03-04T21:10:02.8612924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8614862Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8617522Z 2025-03-04T21:10:02.8617846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8617995Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:10:02.8618073Z 2025-03-04T21:10:02.8618338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8618876Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.8618961Z 2025-03-04T21:10:02.8619246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8621144Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8621219Z 2025-03-04T21:10:02.8621529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8621676Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:10:02.8621753Z 2025-03-04T21:10:02.8622020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8622549Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.8622619Z 2025-03-04T21:10:02.8622911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8624793Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8626082Z 2025-03-04T21:10:02.8626366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8626920Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:10:02.8627002Z 2025-03-04T21:10:02.8627283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8629241Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8629333Z 2025-03-04T21:10:02.8629639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.8629793Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:10:02.8629873Z 2025-03-04T21:10:02.8630176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8630342Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:10:02.8630411Z 2025-03-04T21:10:02.8630688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8631195Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.8631283Z 2025-03-04T21:10:02.8631558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8633317Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8633449Z 2025-03-04T21:10:02.8633734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8633894Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:10:02.8633962Z 2025-03-04T21:10:02.8634222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8634730Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.8634800Z 2025-03-04T21:10:02.8635072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8636854Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8636930Z 2025-03-04T21:10:02.8637223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8637360Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:10:02.8637432Z 2025-03-04T21:10:02.8637681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8638181Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.8638248Z 2025-03-04T21:10:02.8638519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8640296Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8640425Z 2025-03-04T21:10:02.8640717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.8640867Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:10:02.8640940Z 2025-03-04T21:10:02.8641238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8641391Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:10:02.8641457Z 2025-03-04T21:10:02.8641714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8642198Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.8642271Z 2025-03-04T21:10:02.8642537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8644317Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8644393Z 2025-03-04T21:10:02.8644683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8644825Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:10:02.8644889Z 2025-03-04T21:10:02.8645148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8645635Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.8645707Z 2025-03-04T21:10:02.8645974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8647774Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8647879Z 2025-03-04T21:10:02.8648167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8648310Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:10:02.8648374Z 2025-03-04T21:10:02.8648633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8649128Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.8649193Z 2025-03-04T21:10:02.8649467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8651266Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8651342Z 2025-03-04T21:10:02.8651630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.8651779Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:10:02.8651854Z 2025-03-04T21:10:02.8652155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8652313Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:10:02.8652380Z 2025-03-04T21:10:02.8652654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8653179Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.8653281Z 2025-03-04T21:10:02.8653559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8655692Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8655788Z 2025-03-04T21:10:02.8656121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8656298Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:10:02.8656372Z 2025-03-04T21:10:02.8656664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8657210Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.8657294Z 2025-03-04T21:10:02.8657596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8659647Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8659724Z 2025-03-04T21:10:02.8660052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8660200Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:10:02.8660301Z 2025-03-04T21:10:02.8660585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8661152Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.8661248Z 2025-03-04T21:10:02.8661547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8663565Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8663647Z 2025-03-04T21:10:02.8663941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.8664105Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:10:02.8664174Z 2025-03-04T21:10:02.8664480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8664632Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:10:02.8664707Z 2025-03-04T21:10:02.8664973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8665484Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.8665552Z 2025-03-04T21:10:02.8665842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8667725Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8667828Z 2025-03-04T21:10:02.8668136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8668276Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:10:02.8668351Z 2025-03-04T21:10:02.8668634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8669149Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.8669235Z 2025-03-04T21:10:02.8669522Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8671374Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8671442Z 2025-03-04T21:10:02.8671732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8671865Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:10:02.8671938Z 2025-03-04T21:10:02.8672188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8672679Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.8672746Z 2025-03-04T21:10:02.8673015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8674812Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8674910Z 2025-03-04T21:10:02.8675213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.8675360Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:10:02.8675430Z 2025-03-04T21:10:02.8675713Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8675862Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:10:02.8675953Z 2025-03-04T21:10:02.8676205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8676691Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.8676755Z 2025-03-04T21:10:02.8677026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8678909Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8678989Z 2025-03-04T21:10:02.8679292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8679431Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:10:02.8679504Z 2025-03-04T21:10:02.8679755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8680241Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.8680306Z 2025-03-04T21:10:02.8680580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8682381Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8682488Z 2025-03-04T21:10:02.8682801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8682938Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:10:02.8683013Z 2025-03-04T21:10:02.8683270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8683768Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.8683836Z 2025-03-04T21:10:02.8684119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8685909Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8685981Z 2025-03-04T21:10:02.8686278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.8686430Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:10:02.8686506Z 2025-03-04T21:10:02.8686801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8686952Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:10:02.8687020Z 2025-03-04T21:10:02.8687290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8687775Z x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.8687881Z 2025-03-04T21:10:02.8688363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8690215Z x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8690298Z 2025-03-04T21:10:02.8690590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8690734Z out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:10:02.8690800Z 2025-03-04T21:10:02.8691057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8691543Z x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.8691611Z 2025-03-04T21:10:02.8691892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8693754Z x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8693835Z 2025-03-04T21:10:02.8694144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8694286Z out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:10:02.8694361Z 2025-03-04T21:10:02.8694678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8695259Z x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.8695360Z 2025-03-04T21:10:02.8695695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8697707Z x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8697787Z 2025-03-04T21:10:02.8698060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8698580Z x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:10:02.8698660Z 2025-03-04T21:10:02.8698941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8700929Z x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8701012Z 2025-03-04T21:10:02.8701305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.8701464Z x_93 += x_95; out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93; x_93 = x_95 = None 2025-03-04T21:10:02.8701533Z 2025-03-04T21:10:02.8701838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8701986Z out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:10:02.8702081Z 2025-03-04T21:10:02.8702345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8702894Z x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.8702966Z 2025-03-04T21:10:02.8703255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8705165Z x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8705240Z 2025-03-04T21:10:02.8705553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8705695Z out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:10:02.8705772Z 2025-03-04T21:10:02.8706041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8706562Z x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.8706637Z 2025-03-04T21:10:02.8706920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8708819Z x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8708897Z 2025-03-04T21:10:02.8709202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8709368Z out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99); x_99 = None 2025-03-04T21:10:02.8709452Z 2025-03-04T21:10:02.8709723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8710261Z x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.8710339Z 2025-03-04T21:10:02.8710627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8712497Z x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8712575Z 2025-03-04T21:10:02.8712860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.8713024Z x_101 += out_55; out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101; x_101 = out_55 = None 2025-03-04T21:10:02.8713090Z 2025-03-04T21:10:02.8713383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8713528Z out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:10:02.8713598Z 2025-03-04T21:10:02.8713849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8714339Z x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:10:02.8714406Z 2025-03-04T21:10:02.8714686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8716491Z x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8716589Z 2025-03-04T21:10:02.8716896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8717037Z out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:10:02.8717112Z 2025-03-04T21:10:02.8717361Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8717877Z x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:10:02.8717949Z 2025-03-04T21:10:02.8718228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8720018Z x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8720085Z 2025-03-04T21:10:02.8720379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8720516Z out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105); x_105 = None 2025-03-04T21:10:02.8720589Z 2025-03-04T21:10:02.8720839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8721334Z x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:10:02.8721408Z 2025-03-04T21:10:02.8721674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:10:02.8723474Z x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:10:02.8723610Z 2025-03-04T21:10:02.8723890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:10:02.8724052Z x_107 += out_59; out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107; x_107 = out_59 = None 2025-03-04T21:10:02.8724118Z 2025-03-04T21:10:02.8724425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:10:02.8724570Z out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:10:02.8724643Z 2025-03-04T21:10:02.8724893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8725473Z x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T21:10:02.8725540Z 2025-03-04T21:10:02.8725799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8726352Z x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T21:10:02.8726428Z 2025-03-04T21:10:02.8726844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:10:02.8727123Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest'); x_108 = None 2025-03-04T21:10:02.8727190Z 2025-03-04T21:10:02.8727451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8728032Z x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T21:10:02.8728101Z 2025-03-04T21:10:02.8728460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:10:02.8728654Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features; x_110 = top_down_features = None 2025-03-04T21:10:02.8728728Z 2025-03-04T21:10:02.8728979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8729579Z x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T21:10:02.8729659Z 2025-03-04T21:10:02.8730092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:10:02.8730417Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T21:10:02.8730490Z 2025-03-04T21:10:02.8730755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8731338Z x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T21:10:02.8731409Z 2025-03-04T21:10:02.8731756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:10:02.8731977Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1; x_112 = top_down_features_1 = None 2025-03-04T21:10:02.8732042Z 2025-03-04T21:10:02.8732298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8732868Z x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T21:10:02.8732943Z 2025-03-04T21:10:02.8733343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:10:02.8733674Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T21:10:02.8733740Z 2025-03-04T21:10:02.8733996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8734691Z x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T21:10:02.8734778Z 2025-03-04T21:10:02.8735174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:10:02.8735410Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2; x_114 = top_down_features_2 = None 2025-03-04T21:10:02.8735538Z 2025-03-04T21:10:02.8735826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8736514Z x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T21:10:02.8736584Z 2025-03-04T21:10:02.8736973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T21:10:02.8737199Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T21:10:02.8737278Z 2025-03-04T21:10:02.8737756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.8737933Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:10:02.8738003Z 2025-03-04T21:10:02.8738327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8738476Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:10:02.8738554Z 2025-03-04T21:10:02.8739014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.8739187Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:10:02.8739258Z 2025-03-04T21:10:02.8739579Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8739729Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:10:02.8739806Z 2025-03-04T21:10:02.8740203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:02.8740406Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:10:02.8740520Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:10:02.8740651Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:10:02.8740732Z 2025-03-04T21:10:02.8741085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:02.8741232Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:10:02.8741301Z 2025-03-04T21:10:02.8741656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:02.8741787Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:10:02.8741862Z 2025-03-04T21:10:02.8742265Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:02.8742524Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:10:02.8742593Z 2025-03-04T21:10:02.8743077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:02.8743212Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:10:02.8743688Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:10:02.8743825Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:10:02.8743959Z x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:10:02.8744030Z 2025-03-04T21:10:02.8744511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.8744674Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:10:02.8744752Z 2025-03-04T21:10:02.8745063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8745219Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:10:02.8745287Z 2025-03-04T21:10:02.8745746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.8745905Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:10:02.8745984Z 2025-03-04T21:10:02.8746291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8746446Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:10:02.8746516Z 2025-03-04T21:10:02.8746916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:02.8747133Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:10:02.8747246Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:10:02.8747389Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:10:02.8747458Z 2025-03-04T21:10:02.8747811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:02.8747951Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:10:02.8748027Z 2025-03-04T21:10:02.8748370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:02.8748513Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:10:02.8748581Z 2025-03-04T21:10:02.8748986Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:02.8749237Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:10:02.8749330Z 2025-03-04T21:10:02.8749768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:02.8749934Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:10:02.8750379Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:10:02.8750519Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:10:02.8750670Z x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:10:02.8750749Z 2025-03-04T21:10:02.8751199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.8751362Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:10:02.8751429Z 2025-03-04T21:10:02.8751748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8751895Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:10:02.8751970Z 2025-03-04T21:10:02.8752419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.8752577Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:10:02.8752647Z 2025-03-04T21:10:02.8752964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8753099Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:10:02.8753168Z 2025-03-04T21:10:02.8753526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:02.8753721Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:10:02.8753828Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:10:02.8753948Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:10:02.8754018Z 2025-03-04T21:10:02.8754333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:02.8754462Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:10:02.8754526Z 2025-03-04T21:10:02.8754847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:02.8754967Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:10:02.8755037Z 2025-03-04T21:10:02.8755403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:02.8755639Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:10:02.8755727Z 2025-03-04T21:10:02.8756136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:02.8756278Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:10:02.8756690Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:10:02.8756812Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:10:02.8756952Z x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T21:10:02.8757018Z 2025-03-04T21:10:02.8757442Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.8757584Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:10:02.8757661Z 2025-03-04T21:10:02.8757942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8758082Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:10:02.8758146Z 2025-03-04T21:10:02.8758570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.8758713Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:10:02.8758789Z 2025-03-04T21:10:02.8759078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8759220Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:10:02.8759284Z 2025-03-04T21:10:02.8759658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:02.8759857Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:10:02.8759961Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:10:02.8760085Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:10:02.8760151Z 2025-03-04T21:10:02.8760494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:02.8760618Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:10:02.8760689Z 2025-03-04T21:10:02.8761009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:02.8761137Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:10:02.8761201Z 2025-03-04T21:10:02.8761584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:02.8761815Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:10:02.8761902Z 2025-03-04T21:10:02.8762335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:02.8762473Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:10:02.8762884Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:10:02.8763031Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:10:02.8763148Z x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T21:10:02.8763223Z 2025-03-04T21:10:02.8763653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.8763816Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:10:02.8763879Z 2025-03-04T21:10:02.8764174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8764307Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:10:02.8764379Z 2025-03-04T21:10:02.8764799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:02.8764955Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:10:02.8765020Z 2025-03-04T21:10:02.8765323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8765459Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:10:02.8765531Z 2025-03-04T21:10:02.8765900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:02.8766110Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:10:02.8766216Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:10:02.8766332Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:10:02.8766401Z 2025-03-04T21:10:02.8766718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:02.8766845Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:10:02.8766908Z 2025-03-04T21:10:02.8767227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:02.8767345Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:10:02.8767414Z 2025-03-04T21:10:02.8767795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:02.8768022Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:10:02.8768085Z 2025-03-04T21:10:02.8768503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:02.8768628Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:10:02.8769035Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:10:02.8769170Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:10:02.8769292Z x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T21:10:02.8769355Z 2025-03-04T21:10:02.8769655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:10:02.8769782Z tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32); x_116 = None 2025-03-04T21:10:02.8769918Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32); x_117 = None 2025-03-04T21:10:02.8770038Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32); x_118 = None 2025-03-04T21:10:02.8770165Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32); x_119 = None 2025-03-04T21:10:02.8770281Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32); x_120 = None 2025-03-04T21:10:02.8770352Z 2025-03-04T21:10:02.8770606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8771106Z x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_115 = None 2025-03-04T21:10:02.8771171Z 2025-03-04T21:10:02.8771451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:02.8771650Z x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False); x_121 = None 2025-03-04T21:10:02.8771722Z 2025-03-04T21:10:02.8772115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:02.8772635Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:02.8772708Z 2025-03-04T21:10:02.8773071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:02.8773599Z x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_122 = None 2025-03-04T21:10:02.8773682Z 2025-03-04T21:10:02.8773947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8774459Z x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_113 = None 2025-03-04T21:10:02.8774608Z 2025-03-04T21:10:02.8774891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:02.8775114Z x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False); x_123 = None 2025-03-04T21:10:02.8775188Z 2025-03-04T21:10:02.8775646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:02.8776236Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:02.8776308Z 2025-03-04T21:10:02.8776712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:02.8777260Z x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_124 = None 2025-03-04T21:10:02.8777339Z 2025-03-04T21:10:02.8777607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8778122Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_111 = None 2025-03-04T21:10:02.8778190Z 2025-03-04T21:10:02.8778488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:02.8778687Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False); x_125 = None 2025-03-04T21:10:02.8778777Z 2025-03-04T21:10:02.8779154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:02.8779663Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:02.8779736Z 2025-03-04T21:10:02.8780090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:02.8780597Z x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_126 = None 2025-03-04T21:10:02.8780688Z 2025-03-04T21:10:02.8780950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8781451Z x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_109 = None 2025-03-04T21:10:02.8781525Z 2025-03-04T21:10:02.8781795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:02.8781986Z x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False); x_127 = None 2025-03-04T21:10:02.8782052Z 2025-03-04T21:10:02.8782449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:02.8782945Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:02.8783021Z 2025-03-04T21:10:02.8783377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:02.8783881Z x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_128 = None 2025-03-04T21:10:02.8783954Z 2025-03-04T21:10:02.8784208Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:02.8784966Z x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:10:02.8785033Z 2025-03-04T21:10:02.8785312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:02.8785491Z x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False); x_129 = None 2025-03-04T21:10:02.8785566Z 2025-03-04T21:10:02.8785936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:02.8786797Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:10:02.8786868Z 2025-03-04T21:10:02.8787218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:02.8788304Z x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:10:02.8788380Z 2025-03-04T21:10:02.8788736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:10:02.8788903Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:10:02.8789085Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:10:02.8789254Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:10:02.8789410Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:10:02.8789565Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:10:02.8789715Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:10:02.8789863Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:10:02.8790008Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:10:02.8790155Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:10:02.8790297Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:10:02.8790365Z 2025-03-04T21:10:02.8790798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:10:02.8790987Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304); x_131 = None 2025-03-04T21:10:02.8791173Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:10:02.8791361Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:10:02.8791527Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152); x_132 = None 2025-03-04T21:10:02.8791711Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:10:02.8791887Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:10:02.8792047Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76); x_133 = None 2025-03-04T21:10:02.8792217Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:10:02.8792394Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:10:02.8792538Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38); x_134 = None 2025-03-04T21:10:02.8792708Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:10:02.8792903Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:10:02.8793079Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19); x_135 = None 2025-03-04T21:10:02.8793238Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:10:02.8793427Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:10:02.8793494Z 2025-03-04T21:10:02.8793914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:02.8794119Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:10:02.8794195Z 2025-03-04T21:10:02.8794647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:02.8794815Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:10:02.8794973Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:10:02.8795117Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:10:02.8795190Z 2025-03-04T21:10:02.8795568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:02.8795749Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:10:02.8795818Z 2025-03-04T21:10:02.8796141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:02.8796286Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:10:02.8796360Z 2025-03-04T21:10:02.8796677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:02.8796818Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:10:02.8796948Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:02.8797107Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:10:02.8797174Z 2025-03-04T21:10:02.8797505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:02.8797634Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:10:02.8797765Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:10:02.8797918Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:10:02.8797991Z 2025-03-04T21:10:02.8798308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:02.8798441Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:02.8798531Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:10:02.8798666Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:10:02.8798750Z 2025-03-04T21:10:02.8799077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:02.8799243Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:10:02.8799342Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:10:02.8799490Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:10:02.8799573Z 2025-03-04T21:10:02.8799916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:02.8800077Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:02.8800193Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:10:02.8800266Z 2025-03-04T21:10:02.8800584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:02.8800748Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:02.8800861Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:10:02.8800932Z 2025-03-04T21:10:02.8801224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:02.8801382Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:02.8801493Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:10:02.8801565Z 2025-03-04T21:10:02.8801860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:02.8802049Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:10:02.8802169Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:10:02.8802233Z 2025-03-04T21:10:02.8802575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:02.8802717Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:10:02.8802788Z 2025-03-04T21:10:02.8803122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:02.8803270Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:10:02.8803337Z 2025-03-04T21:10:02.8803687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:02.8803829Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:10:02.8803963Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:10:02.8804117Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:10:02.8804262Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:10:02.8804326Z 2025-03-04T21:10:02.8804686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:02.8804844Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:10:02.8804993Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:10:02.8805144Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:10:02.8805287Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:10:02.8805367Z 2025-03-04T21:10:02.8805709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:02.8805830Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:10:02.8805997Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:10:02.8806151Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:10:02.8806227Z 2025-03-04T21:10:02.8806561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:02.8806688Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:10:02.8806857Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:10:02.8807001Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:10:02.8807066Z 2025-03-04T21:10:02.8807386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:02.8807487Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:10:02.8807618Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:10:02.8807682Z 2025-03-04T21:10:02.8808000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:02.8808096Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:10:02.8808220Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:10:02.8808287Z 2025-03-04T21:10:02.8808599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:02.8808714Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:10:02.8808852Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:10:02.8808918Z 2025-03-04T21:10:02.8809240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:02.8809358Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:10:02.8809492Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:10:02.8809557Z 2025-03-04T21:10:02.8809908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:02.8810094Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:10:02.8810166Z 2025-03-04T21:10:02.8810509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:02.8810696Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:10:02.8810782Z 2025-03-04T21:10:02.8811165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:02.8811369Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:10:02.8811435Z 2025-03-04T21:10:02.8811842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:02.8812053Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:10:02.8812126Z 2025-03-04T21:10:02.8812575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:02.8812739Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:10:02.8812891Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:10:02.8813041Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:10:02.8813107Z 2025-03-04T21:10:02.8813484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:02.8813656Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:10:02.8813733Z 2025-03-04T21:10:02.8814043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:02.8814201Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:10:02.8814265Z 2025-03-04T21:10:02.8814653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:02.8814810Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:10:02.8814965Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:10:02.8815135Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:10:02.8815216Z 2025-03-04T21:10:02.8815575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:02.8815728Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:10:02.8815867Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:10:02.8816048Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:10:02.8816121Z 2025-03-04T21:10:02.8816457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:02.8816581Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:10:02.8816684Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:10:02.8816819Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:10:02.8817316Z 2025-03-04T21:10:02.8817630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:02.8817806Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:10:02.8817900Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:10:02.8818056Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:10:02.8818123Z 2025-03-04T21:10:02.8818434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:02.8818595Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:02.8818712Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:10:02.8818806Z 2025-03-04T21:10:02.8819108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:02.8819271Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:02.8819388Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:10:02.8819461Z 2025-03-04T21:10:02.8819763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:02.8819920Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:02.8820032Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:10:02.8820103Z 2025-03-04T21:10:02.8820410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:02.8820605Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:10:02.8820715Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:10:02.8820786Z 2025-03-04T21:10:02.8821125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:02.8821274Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:10:02.8821339Z 2025-03-04T21:10:02.8821674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:02.8821817Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:10:02.8821890Z 2025-03-04T21:10:02.8822235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:02.8822381Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:10:02.8822510Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:10:02.8822678Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:10:02.8822821Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:10:02.8822893Z 2025-03-04T21:10:02.8823242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:02.8823406Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:10:02.8823586Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:10:02.8823749Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:10:02.8823918Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:10:02.8823986Z 2025-03-04T21:10:02.8824327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:02.8824446Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:10:02.8824637Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:10:02.8824779Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:10:02.8824854Z 2025-03-04T21:10:02.8825188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:02.8825310Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:10:02.8825480Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:10:02.8825625Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:10:02.8825690Z 2025-03-04T21:10:02.8826009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:02.8826113Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:10:02.8826245Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:10:02.8826312Z 2025-03-04T21:10:02.8826626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:02.8826722Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:10:02.8826849Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:10:02.8826914Z 2025-03-04T21:10:02.8827227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:02.8827346Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:10:02.8827490Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:10:02.8827558Z 2025-03-04T21:10:02.8827882Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:02.8828002Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:10:02.8828140Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:10:02.8828207Z 2025-03-04T21:10:02.8828563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:02.8828757Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:10:02.8828829Z 2025-03-04T21:10:02.8829179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:02.8829371Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:10:02.8829436Z 2025-03-04T21:10:02.8829822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:02.8830013Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:10:02.8830088Z 2025-03-04T21:10:02.8830487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:02.8830719Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:10:02.8830786Z 2025-03-04T21:10:02.8831224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:02.8831385Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:10:02.8831537Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:10:02.8831685Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:10:02.8831750Z 2025-03-04T21:10:02.8832127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:02.8832299Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:10:02.8832372Z 2025-03-04T21:10:02.8832686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:02.8832839Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:10:02.8832903Z 2025-03-04T21:10:02.8833222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:02.8833351Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:10:02.8833486Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:10:02.8833635Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:10:02.8833710Z 2025-03-04T21:10:02.8834028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:02.8834158Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:10:02.8834278Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:10:02.8834437Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:10:02.8834502Z 2025-03-04T21:10:02.8834819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:02.8834940Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:10:02.8835038Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:10:02.8835203Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:10:02.8835290Z 2025-03-04T21:10:02.8835608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:02.8835765Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:10:02.8835883Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:10:02.8836027Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:10:02.8836094Z 2025-03-04T21:10:02.8836419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:02.8836573Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:02.8836712Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:10:02.8836779Z 2025-03-04T21:10:02.8837088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:02.8837240Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:02.8837363Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:10:02.8837428Z 2025-03-04T21:10:02.8837735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:02.8837883Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:02.8838003Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:10:02.8838070Z 2025-03-04T21:10:02.8838391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:02.8838592Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:10:02.8838705Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:10:02.8839569Z 2025-03-04T21:10:02.8839987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:02.8840152Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:10:02.8840221Z 2025-03-04T21:10:02.8840571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:02.8840715Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:10:02.8840792Z 2025-03-04T21:10:02.8841140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:02.8841287Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:10:02.8841416Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:10:02.8841585Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:10:02.8841730Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:10:02.8841829Z 2025-03-04T21:10:02.8842180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:02.8842346Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:10:02.8842469Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:10:02.8842646Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:10:02.8842786Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:10:02.8842858Z 2025-03-04T21:10:02.8843190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:02.8843315Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:10:02.8843492Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:10:02.8843638Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:10:02.8843703Z 2025-03-04T21:10:02.8844052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:02.8844167Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:10:02.8844342Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:10:02.8844476Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:10:02.8844548Z 2025-03-04T21:10:02.8844864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:02.8844972Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:10:02.8845094Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:10:02.8845166Z 2025-03-04T21:10:02.8845473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:02.8845579Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:10:02.8845697Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:10:02.8845770Z 2025-03-04T21:10:02.8846075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:02.8846200Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:10:02.8846337Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:10:02.8846411Z 2025-03-04T21:10:02.8846714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:02.8846838Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:10:02.8846976Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:10:02.8847041Z 2025-03-04T21:10:02.8847394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:02.8847583Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:10:02.8847671Z 2025-03-04T21:10:02.8848011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:02.8848214Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:10:02.8848280Z 2025-03-04T21:10:02.8848689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:02.8848868Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:10:02.8848940Z 2025-03-04T21:10:02.8849350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:02.8849583Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:10:02.8849653Z 2025-03-04T21:10:02.8850105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:02.8850261Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:10:02.8850419Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:10:02.8850560Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:10:02.8850635Z 2025-03-04T21:10:02.8851021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:02.8851204Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:10:02.8851271Z 2025-03-04T21:10:02.8851604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:02.8851755Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:10:02.8851829Z 2025-03-04T21:10:02.8852149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:02.8852286Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:10:02.8852413Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:10:02.8852576Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:10:02.8852644Z 2025-03-04T21:10:02.8852979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:02.8853106Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:10:02.8853239Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:10:02.8853392Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:10:02.8853466Z 2025-03-04T21:10:02.8853784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:02.8853916Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:10:02.8854040Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:10:02.8854181Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:10:02.8854270Z 2025-03-04T21:10:02.8854651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:02.8854837Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:10:02.8854938Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:10:02.8855078Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:10:02.8855146Z 2025-03-04T21:10:02.8855467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:02.8855649Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:02.8855780Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:10:02.8855852Z 2025-03-04T21:10:02.8856181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:02.8856347Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:02.8856476Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:10:02.8856545Z 2025-03-04T21:10:02.8856876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:02.8857033Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:02.8857159Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:10:02.8857227Z 2025-03-04T21:10:02.8857550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:02.8857742Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:10:02.8857869Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:10:02.8857936Z 2025-03-04T21:10:02.8858290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:02.8858436Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:10:02.8858513Z 2025-03-04T21:10:02.8858856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:02.8859008Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:10:02.8859073Z 2025-03-04T21:10:02.8859436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:02.8859575Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:10:02.8859711Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:10:02.8859869Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:10:02.8860023Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:10:02.8860109Z 2025-03-04T21:10:02.8860476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:02.8860641Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:10:02.8860767Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:10:02.8860948Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:10:02.8861090Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:10:02.8861165Z 2025-03-04T21:10:02.8861510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:02.8861655Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:10:02.8861821Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:10:02.8861970Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:10:02.8862037Z 2025-03-04T21:10:02.8862386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:02.8862502Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:10:02.8862680Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:10:02.8862816Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:10:02.8862888Z 2025-03-04T21:10:02.8863210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:02.8863321Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:10:02.8863441Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:10:02.8863515Z 2025-03-04T21:10:02.8863835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:02.8863938Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:10:02.8864058Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:10:02.8864130Z 2025-03-04T21:10:02.8864443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:02.8864571Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:10:02.8864708Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:10:02.8864780Z 2025-03-04T21:10:02.8865089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:02.8865219Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:10:02.8865354Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:10:02.8865429Z 2025-03-04T21:10:02.8865789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:02.8865992Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:10:02.8866076Z 2025-03-04T21:10:02.8866424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:02.8866607Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:10:02.8866680Z 2025-03-04T21:10:02.8867096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:02.8867284Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:10:02.8867350Z 2025-03-04T21:10:02.8867786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:02.8868003Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:10:02.8868070Z 2025-03-04T21:10:02.8868506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:02.8868657Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:10:02.8868812Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:10:02.8868947Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:10:02.8869019Z 2025-03-04T21:10:02.8869390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:02.8869567Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:10:02.8869633Z 2025-03-04T21:10:02.8869952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:02.8870098Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:10:02.8870170Z 2025-03-04T21:10:02.8870483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:02.8870619Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:10:02.8870747Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:10:02.8870904Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:10:02.8870970Z 2025-03-04T21:10:02.8871297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:02.8871422Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:10:02.8871550Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:10:02.8871700Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:10:02.8871772Z 2025-03-04T21:10:02.8872084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:02.8872238Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:10:02.8872330Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:10:02.8872489Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:10:02.8872554Z 2025-03-04T21:10:02.8872872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:02.8873036Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:10:02.8873137Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:10:02.8873266Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:10:02.8873339Z 2025-03-04T21:10:02.8873638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:02.8873820Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:02.8873936Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:10:02.8874007Z 2025-03-04T21:10:02.8874304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:02.8874463Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:02.8874575Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:10:02.8874651Z 2025-03-04T21:10:02.8874947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:02.8875103Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:02.8875222Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:10:02.8875289Z 2025-03-04T21:10:02.8875599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:02.8875778Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:10:02.8875896Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:10:02.8875962Z 2025-03-04T21:10:02.8876304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:02.8876444Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:10:02.8876517Z 2025-03-04T21:10:02.8876848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:02.8876990Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:10:02.8877056Z 2025-03-04T21:10:02.8877405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:02.8877539Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:10:02.8877668Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:10:02.8877820Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:10:02.8877987Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:10:02.8878053Z 2025-03-04T21:10:02.8878409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:02.8878561Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:10:02.8878705Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:10:02.8878858Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:10:02.8879002Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:10:02.8879065Z 2025-03-04T21:10:02.8879400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:02.8879533Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:10:02.8879703Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:10:02.8879837Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:10:02.8879908Z 2025-03-04T21:10:02.8880239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:02.8880360Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:10:02.8880527Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:10:02.8880666Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:10:02.8880733Z 2025-03-04T21:10:02.8881051Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:02.8881152Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:10:02.8881275Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:10:02.8881339Z 2025-03-04T21:10:02.8881654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:02.8881749Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:10:02.8881869Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:10:02.8881934Z 2025-03-04T21:10:02.8882246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:02.8882362Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:10:02.8882499Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:10:02.8882564Z 2025-03-04T21:10:02.8882874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:02.8882995Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:10:02.8883123Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:10:02.8883194Z 2025-03-04T21:10:02.8883544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:02.8883743Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:10:02.8883825Z 2025-03-04T21:10:02.8884182Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:02.8884343Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:10:02.8884416Z 2025-03-04T21:10:02.8884817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:02.8884997Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:10:02.8885063Z 2025-03-04T21:10:02.8885568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:10:02.8885706Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:10:02.8885777Z 2025-03-04T21:10:02.8886069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8886218Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:10:02.8886281Z 2025-03-04T21:10:02.8886716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:02.8886828Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:10:02.8886942Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:10:02.8887056Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:10:02.8887130Z 2025-03-04T21:10:02.8887587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:02.8887728Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:02.8887957Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:10:02.8888029Z 2025-03-04T21:10:02.8888735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:02.8888925Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:02.8888993Z 2025-03-04T21:10:02.8889298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8889432Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:10:02.8889501Z 2025-03-04T21:10:02.8889943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:02.8890061Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:10:02.8890178Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:10:02.8890354Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:10:02.8890427Z 2025-03-04T21:10:02.8890889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:02.8891058Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:02.8891323Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:10:02.8891398Z 2025-03-04T21:10:02.8891852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:02.8892058Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:02.8892128Z 2025-03-04T21:10:02.8892433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8892562Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:10:02.8892635Z 2025-03-04T21:10:02.8893071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:02.8893193Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:10:02.8893300Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:10:02.8893425Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:10:02.8893491Z 2025-03-04T21:10:02.8893960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:02.8894095Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:02.8894360Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:10:02.8894434Z 2025-03-04T21:10:02.8895055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:02.8895244Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:02.8895326Z 2025-03-04T21:10:02.8895655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8895807Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:10:02.8895874Z 2025-03-04T21:10:02.8896326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:02.8896453Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:10:02.8896563Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:10:02.8896693Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:10:02.8896760Z 2025-03-04T21:10:02.8897237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:02.8897411Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:02.8897662Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:10:02.8897731Z 2025-03-04T21:10:02.8898223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:02.8898393Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:02.8898470Z 2025-03-04T21:10:02.8898794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8898932Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:10:02.8898999Z 2025-03-04T21:10:02.8899443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:02.8899571Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:10:02.8899684Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:10:02.8899802Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:10:02.8899875Z 2025-03-04T21:10:02.8900328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:02.8900505Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:10:02.8900740Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:10:02.8900816Z 2025-03-04T21:10:02.8901280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:02.8901449Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:02.8901515Z 2025-03-04T21:10:02.8901827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:02.8901954Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:10:02.8902030Z 2025-03-04T21:10:02.8902319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:02.8902711Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:10:02.8902786Z 2025-03-04T21:10:02.8903074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:02.8903553Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:10:02.8903654Z 2025-03-04T21:10:02.8903941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:02.8904142Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:10:02.8904230Z 2025-03-04T21:10:02.8904621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:10:02.8904772Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:10:02.8904838Z 2025-03-04T21:10:02.8905165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:10:02.8905317Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:10:02.8905392Z 2025-03-04T21:10:02.8905765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:10:02.8905908Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:10:02.8905972Z 2025-03-04T21:10:02.8906461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:10:02.8906599Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:10:02.8906732Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:10:02.8906887Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:10:02.8907030Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:10:02.8907095Z 2025-03-04T21:10:02.8907467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:10:02.8907584Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:10:02.8907658Z 2025-03-04T21:10:14.6665216Z 2025-03-04T21:10:14.6665815Z class GraphModule(torch.nn.Module): 2025-03-04T21:10:14.6668367Z def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:10:14.6671361Z l_features_p2_ = L_features_p2_ 2025-03-04T21:10:14.6671598Z l_features_p3_ = L_features_p3_ 2025-03-04T21:10:14.6671826Z l_features_p4_ = L_features_p4_ 2025-03-04T21:10:14.6672047Z l_features_p5_ = L_features_p5_ 2025-03-04T21:10:14.6672259Z l_features_p6_ = L_features_p6_ 2025-03-04T21:10:14.6672715Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:10:14.6673315Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:10:14.6673915Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:10:14.6674546Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:10:14.6675133Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:10:14.6675693Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:10:14.6676213Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:10:14.6676787Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:10:14.6677411Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:10:14.6678013Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:10:14.6678625Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:10:14.6679018Z 2025-03-04T21:10:14.6679632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.6680331Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:10:14.6680625Z 2025-03-04T21:10:14.6681048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.6681577Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:10:14.6681856Z 2025-03-04T21:10:14.6682423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.6683103Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:10:14.6683379Z 2025-03-04T21:10:14.6683769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.6684264Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:10:14.6684531Z 2025-03-04T21:10:14.6685022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:14.6685703Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:10:14.6686061Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:10:14.6686376Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:10:14.6686635Z 2025-03-04T21:10:14.6687088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:14.6687637Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:10:14.6687894Z 2025-03-04T21:10:14.6688591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:14.6689140Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:10:14.6689404Z 2025-03-04T21:10:14.6689945Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:14.6690644Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:10:14.6690997Z 2025-03-04T21:10:14.6691537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:14.6692172Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:10:14.6692702Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:10:14.6693232Z add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:10:14.6693552Z x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:10:14.6693799Z 2025-03-04T21:10:14.6694364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.6695170Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:10:14.6695454Z 2025-03-04T21:10:14.6695866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.6696391Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:10:14.6696673Z 2025-03-04T21:10:14.6697228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.6697901Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:10:14.6698189Z 2025-03-04T21:10:14.6698597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.6699117Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:10:14.6699391Z 2025-03-04T21:10:14.6699877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:14.6700577Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:10:14.6700951Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:10:14.6701388Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:10:14.6701648Z 2025-03-04T21:10:14.6702098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:14.6702679Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:10:14.6702955Z 2025-03-04T21:10:14.6703400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:14.6703945Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:10:14.6704211Z 2025-03-04T21:10:14.6704732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:14.6705423Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:10:14.6705777Z 2025-03-04T21:10:14.6706306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:14.6706943Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:10:14.6707489Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:10:14.6707986Z add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:10:14.6708293Z x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4); add_1 = None 2025-03-04T21:10:14.6708535Z 2025-03-04T21:10:14.6709057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.6709693Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:10:14.6709963Z 2025-03-04T21:10:14.6710348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.6710840Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:10:14.6711105Z 2025-03-04T21:10:14.6711630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.6712264Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:10:14.6712536Z 2025-03-04T21:10:14.6712927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.6713419Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:10:14.6713679Z 2025-03-04T21:10:14.6714146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:14.6714794Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:10:14.6715149Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:10:14.6715442Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:10:14.6715687Z 2025-03-04T21:10:14.6716111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:14.6716659Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:10:14.6716908Z 2025-03-04T21:10:14.6717323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:14.6717827Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:10:14.6718074Z 2025-03-04T21:10:14.6718561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:14.6719209Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:10:14.6719542Z 2025-03-04T21:10:14.6720045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:14.6720646Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:10:14.6721150Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:10:14.6721643Z add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:10:14.6721950Z x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4); add_2 = None 2025-03-04T21:10:14.6722195Z 2025-03-04T21:10:14.6722707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.6723352Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:10:14.6723618Z 2025-03-04T21:10:14.6724009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.6724502Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:10:14.6724772Z 2025-03-04T21:10:14.6725293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.6725905Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:10:14.6726167Z 2025-03-04T21:10:14.6726540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.6727015Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:10:14.6727273Z 2025-03-04T21:10:14.6727736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:14.6728401Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:10:14.6728750Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:10:14.6729055Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:10:14.6729293Z 2025-03-04T21:10:14.6729701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:14.6730219Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:10:14.6730466Z 2025-03-04T21:10:14.6730876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:14.6731379Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:10:14.6731627Z 2025-03-04T21:10:14.6732117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:14.6732768Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:10:14.6733104Z 2025-03-04T21:10:14.6733604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:14.6734199Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:10:14.6734797Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:10:14.6735322Z add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:10:14.6735644Z x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:10:14.6735898Z 2025-03-04T21:10:14.6736455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.6737113Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:10:14.6737387Z 2025-03-04T21:10:14.6737796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.6738288Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:10:14.6738551Z 2025-03-04T21:10:14.6739072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.6739692Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:10:14.6739959Z 2025-03-04T21:10:14.6740349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.6740850Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:10:14.6741110Z 2025-03-04T21:10:14.6741565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:14.6742216Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:10:14.6742565Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:10:14.6742866Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:10:14.6743117Z 2025-03-04T21:10:14.6743542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:14.6744083Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:10:14.6744338Z 2025-03-04T21:10:14.6744760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:14.6745273Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:10:14.6745525Z 2025-03-04T21:10:14.6746025Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:14.6746688Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:10:14.6747028Z 2025-03-04T21:10:14.6747545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:14.6748156Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:10:14.6748670Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:10:14.6749177Z add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:10:14.6749488Z x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:10:14.6749738Z 2025-03-04T21:10:14.6750141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:10:14.6750642Z tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:10:14.6750966Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32); x_1 = None 2025-03-04T21:10:14.6751285Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32); x_2 = None 2025-03-04T21:10:14.6751595Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32); x_3 = None 2025-03-04T21:10:14.6751902Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32); x_4 = None 2025-03-04T21:10:14.6752159Z 2025-03-04T21:10:14.6752524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:14.6753291Z x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p2_ = None 2025-03-04T21:10:14.6753868Z 2025-03-04T21:10:14.6754257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:14.6754791Z x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False); x_5 = None 2025-03-04T21:10:14.6755108Z 2025-03-04T21:10:14.6755591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:14.6756478Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:14.6757039Z 2025-03-04T21:10:14.6757515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:14.6758352Z x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_6 = None 2025-03-04T21:10:14.6758881Z 2025-03-04T21:10:14.6759246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:14.6759979Z x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p3_ = None 2025-03-04T21:10:14.6760507Z 2025-03-04T21:10:14.6760871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:14.6761392Z x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False); x_7 = None 2025-03-04T21:10:14.6761697Z 2025-03-04T21:10:14.6762163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:14.6763004Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:14.6763528Z 2025-03-04T21:10:14.6763975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:14.6764796Z x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_8 = None 2025-03-04T21:10:14.6765317Z 2025-03-04T21:10:14.6765652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:14.6766372Z x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p4_ = None 2025-03-04T21:10:14.6766884Z 2025-03-04T21:10:14.6767243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:14.6767752Z x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False); x_9 = None 2025-03-04T21:10:14.6768041Z 2025-03-04T21:10:14.6768504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:14.6769334Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:14.6769877Z 2025-03-04T21:10:14.6770366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:14.6771197Z x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_10 = None 2025-03-04T21:10:14.6771704Z 2025-03-04T21:10:14.6772049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:14.6773738Z x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p5_ = None 2025-03-04T21:10:14.6774293Z 2025-03-04T21:10:14.6774769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:14.6775324Z x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False); x_11 = None 2025-03-04T21:10:14.6775645Z 2025-03-04T21:10:14.6776144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:14.6776983Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:14.6777510Z 2025-03-04T21:10:14.6777985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:14.6778822Z x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_12 = None 2025-03-04T21:10:14.6779345Z 2025-03-04T21:10:14.6779708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:14.6780630Z x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:10:14.6781340Z 2025-03-04T21:10:14.6781718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:14.6782245Z x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False); x_13 = None 2025-03-04T21:10:14.6782553Z 2025-03-04T21:10:14.6783040Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:14.6784143Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:10:14.6784943Z 2025-03-04T21:10:14.6785411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:14.6786486Z x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:10:14.6787207Z 2025-03-04T21:10:14.6787650Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:10:14.6788428Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:10:14.6788813Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:10:14.6789192Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:10:14.6789572Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:10:14.6789935Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:10:14.6790285Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:10:14.6790637Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:10:14.6790984Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:10:14.6791339Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:10:14.6791681Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:10:14.6791950Z 2025-03-04T21:10:14.6792478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:10:14.6793138Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304); x_15 = None 2025-03-04T21:10:14.6793562Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:10:14.6793993Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:10:14.6794404Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152); x_16 = None 2025-03-04T21:10:14.6794813Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:10:14.6795211Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:10:14.6795587Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76); x_17 = None 2025-03-04T21:10:14.6795946Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:10:14.6796320Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:10:14.6796678Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38); x_18 = None 2025-03-04T21:10:14.6797030Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:10:14.6797448Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:10:14.6797829Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19); x_19 = None 2025-03-04T21:10:14.6798178Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:10:14.6798575Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:10:14.6798863Z 2025-03-04T21:10:14.6799358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:14.6800031Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:10:14.6800354Z 2025-03-04T21:10:14.6800871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:14.6801505Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:10:14.6801865Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:10:14.6802208Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:10:14.6802471Z 2025-03-04T21:10:14.6802928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:14.6803514Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:10:14.6803803Z 2025-03-04T21:10:14.6804204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:14.6804708Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:10:14.6804974Z 2025-03-04T21:10:14.6805373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:14.6805873Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:10:14.6806188Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:14.6806520Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:10:14.6806794Z 2025-03-04T21:10:14.6807187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:14.6807686Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:10:14.6807987Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:10:14.6808321Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:10:14.6808598Z 2025-03-04T21:10:14.6808993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:14.6809490Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:14.6809770Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:10:14.6810076Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:10:14.6810324Z 2025-03-04T21:10:14.6810732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:14.6811275Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:10:14.6811576Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:10:14.6811877Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:10:14.6812139Z 2025-03-04T21:10:14.6812578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:14.6813107Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:14.6813473Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:10:14.6813720Z 2025-03-04T21:10:14.6814110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:14.6814695Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:14.6815033Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:10:14.6815274Z 2025-03-04T21:10:14.6815663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:14.6816176Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:14.6816505Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:10:14.6816746Z 2025-03-04T21:10:14.6817152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:14.6817720Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:10:14.6818086Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:10:14.6818332Z 2025-03-04T21:10:14.6818784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:14.6819326Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:10:14.6819596Z 2025-03-04T21:10:14.6820020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:14.6820562Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:10:14.6820822Z 2025-03-04T21:10:14.6821259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:14.6821808Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:10:14.6822139Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:10:14.6822479Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:10:14.6822833Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:10:14.6823097Z 2025-03-04T21:10:14.6823537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:14.6824129Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:10:14.6824477Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:10:14.6824817Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:10:14.6825191Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:10:14.6825458Z 2025-03-04T21:10:14.6825879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:14.6826399Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:10:14.6826751Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:10:14.6827108Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:10:14.6827369Z 2025-03-04T21:10:14.6827794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:14.6828302Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:10:14.6828642Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:10:14.6829004Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:10:14.6829267Z 2025-03-04T21:10:14.6829670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:14.6830147Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:10:14.6830417Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:10:14.6830659Z 2025-03-04T21:10:14.6831054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:14.6831531Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:10:14.6831790Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:10:14.6832023Z 2025-03-04T21:10:14.6832410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:14.6832871Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:10:14.6833166Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:10:14.6833417Z 2025-03-04T21:10:14.6833800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:14.6834267Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:10:14.6834559Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:10:14.6834805Z 2025-03-04T21:10:14.6835237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:14.6835807Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:10:14.6836098Z 2025-03-04T21:10:14.6836510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:14.6837079Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:10:14.6837384Z 2025-03-04T21:10:14.6837860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:14.6838483Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:10:14.6838774Z 2025-03-04T21:10:14.6839264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:14.6839936Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:10:14.6840283Z 2025-03-04T21:10:14.6840805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:14.6841433Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:10:14.6841791Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:10:14.6842134Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:10:14.6842392Z 2025-03-04T21:10:14.6842839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:14.6843420Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:10:14.6843708Z 2025-03-04T21:10:14.6844098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:14.6844599Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:10:14.6844871Z 2025-03-04T21:10:14.6845264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:14.6845755Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:10:14.6846066Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:10:14.6846390Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:10:14.6846659Z 2025-03-04T21:10:14.6847057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:14.6847547Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:10:14.6847849Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:10:14.6848179Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:10:14.6848451Z 2025-03-04T21:10:14.6848839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:14.6849317Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:10:14.6849590Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:10:14.6849896Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:10:14.6850153Z 2025-03-04T21:10:14.6850551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:14.6851079Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:10:14.6851376Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:10:14.6851669Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:10:14.6851922Z 2025-03-04T21:10:14.6852314Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:14.6852814Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:14.6853159Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:10:14.6853397Z 2025-03-04T21:10:14.6853780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:14.6854361Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:14.6854789Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:10:14.6855056Z 2025-03-04T21:10:14.6855487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:14.6856019Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:14.6856361Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:10:14.6856625Z 2025-03-04T21:10:14.6857054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:14.6857648Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:10:14.6858028Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:10:14.6858282Z 2025-03-04T21:10:14.6858748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:14.6859329Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:10:14.6859612Z 2025-03-04T21:10:14.6860069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:14.6860644Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:10:14.6860928Z 2025-03-04T21:10:14.6861404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:14.6861993Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:10:14.6862341Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:10:14.6862710Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:10:14.6863099Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:10:14.6863384Z 2025-03-04T21:10:14.6863894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:14.6864488Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:10:14.6864829Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:10:14.6865185Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:10:14.6865547Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:10:14.6865812Z 2025-03-04T21:10:14.6866234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:14.6866743Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:10:14.6867121Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:10:14.6867478Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:10:14.6867740Z 2025-03-04T21:10:14.6868168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:14.6868677Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:10:14.6869017Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:10:14.6869376Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:10:14.6869647Z 2025-03-04T21:10:14.6870043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:14.6870500Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:10:14.6870766Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:10:14.6871003Z 2025-03-04T21:10:14.6871389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:14.6871844Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:10:14.6872110Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:10:14.6872344Z 2025-03-04T21:10:14.6872727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:14.6873199Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:10:14.6873505Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:10:14.6873755Z 2025-03-04T21:10:14.6874145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:14.6874627Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:10:14.6874936Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:10:14.6875192Z 2025-03-04T21:10:14.6875634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:14.6876217Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:10:14.6876549Z 2025-03-04T21:10:14.6876973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:14.6877550Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:10:14.6877842Z 2025-03-04T21:10:14.6878333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:14.6878954Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:10:14.6879248Z 2025-03-04T21:10:14.6879723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:14.6880400Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:10:14.6880729Z 2025-03-04T21:10:14.6881247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:14.6881884Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:10:14.6882245Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:10:14.6882589Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:10:14.6882854Z 2025-03-04T21:10:14.6883311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:14.6883899Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:10:14.6884186Z 2025-03-04T21:10:14.6884579Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:14.6885092Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:10:14.6885361Z 2025-03-04T21:10:14.6885761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:14.6886263Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:10:14.6886575Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:10:14.6886904Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:10:14.6887175Z 2025-03-04T21:10:14.6887583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:14.6888187Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:10:14.6888513Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:10:14.6888850Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:10:14.6889136Z 2025-03-04T21:10:14.6889549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:14.6890052Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:10:14.6890403Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:10:14.6890691Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:10:14.6890994Z 2025-03-04T21:10:14.6891416Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:14.6891988Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:10:14.6892305Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:10:14.6892594Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:10:14.6892856Z 2025-03-04T21:10:14.6893269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:14.6893840Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:14.6894198Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:10:14.6894461Z 2025-03-04T21:10:14.6894981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:14.6895562Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:14.6895966Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:10:14.6896322Z 2025-03-04T21:10:14.6896892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:14.6897436Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:14.6897789Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:10:14.6898042Z 2025-03-04T21:10:14.6898459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:14.6899047Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:10:14.6899430Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:10:14.6899677Z 2025-03-04T21:10:14.6900133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:14.6900713Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:10:14.6900993Z 2025-03-04T21:10:14.6901443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:14.6902011Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:10:14.6902288Z 2025-03-04T21:10:14.6902753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:14.6903334Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:10:14.6903681Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:10:14.6904042Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:10:14.6904419Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:10:14.6904730Z 2025-03-04T21:10:14.6905190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:14.6905783Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:10:14.6906122Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:10:14.6906496Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:10:14.6906873Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:10:14.6907158Z 2025-03-04T21:10:14.6907598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:14.6908150Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:10:14.6908508Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:10:14.6908893Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:10:14.6909156Z 2025-03-04T21:10:14.6909580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:14.6910088Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:10:14.6910430Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:10:14.6910791Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:10:14.6911055Z 2025-03-04T21:10:14.6911466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:14.6911944Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:10:14.6912217Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:10:14.6912464Z 2025-03-04T21:10:14.6912867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:14.6913334Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:10:14.6913602Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:10:14.6913842Z 2025-03-04T21:10:14.6914241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:14.6914733Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:10:14.6915045Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:10:14.6915305Z 2025-03-04T21:10:14.6915706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:14.6916183Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:10:14.6916488Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:10:14.6916747Z 2025-03-04T21:10:14.6917183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:14.6917785Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:10:14.6918194Z 2025-03-04T21:10:14.6918619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:14.6919187Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:10:14.6919476Z 2025-03-04T21:10:14.6919977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:14.6920592Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:10:14.6920890Z 2025-03-04T21:10:14.6921393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:14.6922054Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:10:14.6922381Z 2025-03-04T21:10:14.6922889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:14.6923524Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:10:14.6923880Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:10:14.6924226Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:10:14.6924487Z 2025-03-04T21:10:14.6924948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:14.6925543Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:10:14.6925832Z 2025-03-04T21:10:14.6926225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:14.6926741Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:10:14.6927005Z 2025-03-04T21:10:14.6927391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:14.6927877Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:10:14.6928183Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:10:14.6928508Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:10:14.6928777Z 2025-03-04T21:10:14.6929176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:14.6929662Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:10:14.6929965Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:10:14.6930289Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:10:14.6930554Z 2025-03-04T21:10:14.6930941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:14.6931441Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:10:14.6931712Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:10:14.6932006Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:10:14.6932264Z 2025-03-04T21:10:14.6932667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:14.6933204Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:10:14.6933506Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:10:14.6933788Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:10:14.6934039Z 2025-03-04T21:10:14.6934431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:14.6935046Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:14.6935412Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:10:14.6935681Z 2025-03-04T21:10:14.6936120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:14.6936690Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:14.6937035Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:10:14.6950650Z 2025-03-04T21:10:14.6951176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:14.6951744Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:14.6952102Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:10:14.6952364Z 2025-03-04T21:10:14.6952780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:14.6953333Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:10:14.6953698Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:10:14.6953941Z 2025-03-04T21:10:14.6954383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:14.6954926Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:10:14.6955198Z 2025-03-04T21:10:14.6955629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:14.6956170Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:10:14.6956436Z 2025-03-04T21:10:14.6956878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:14.6957423Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:10:14.6957754Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:10:14.6958100Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:10:14.6958604Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:10:14.6958876Z 2025-03-04T21:10:14.6959354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:14.6959906Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:10:14.6960274Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:10:14.6960607Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:10:14.6960955Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:10:14.6961214Z 2025-03-04T21:10:14.6961655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:14.6962169Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:10:14.6962509Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:10:14.6962867Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:10:14.6963125Z 2025-03-04T21:10:14.6963552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:14.6964058Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:10:14.6964400Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:10:14.6964761Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:10:14.6965027Z 2025-03-04T21:10:14.6965438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:14.6965919Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:10:14.6966199Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:10:14.6966448Z 2025-03-04T21:10:14.6966850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:14.6967316Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:10:14.6967586Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:10:14.6967833Z 2025-03-04T21:10:14.6968232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:14.6968723Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:10:14.6969037Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:10:14.6969297Z 2025-03-04T21:10:14.6969695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:14.6970186Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:10:14.6970491Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:10:14.6970749Z 2025-03-04T21:10:14.6971190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:14.6971812Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:10:14.6972160Z 2025-03-04T21:10:14.6972587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:14.6973164Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:10:14.6973465Z 2025-03-04T21:10:14.6973977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:14.6974704Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:10:14.6975022Z 2025-03-04T21:10:14.6975554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:14.6976271Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:10:14.6976612Z 2025-03-04T21:10:14.6977156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:14.6977830Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:10:14.6978211Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:10:14.6978578Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:10:14.6978855Z 2025-03-04T21:10:14.6979342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:14.6979970Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:10:14.6980271Z 2025-03-04T21:10:14.6980695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:14.6981235Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:10:14.6981518Z 2025-03-04T21:10:14.6981939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:14.6982470Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:10:14.6982803Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:10:14.6983150Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:10:14.6983435Z 2025-03-04T21:10:14.6983865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:14.6984395Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:10:14.6984714Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:10:14.6985061Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:10:14.6985339Z 2025-03-04T21:10:14.6985759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:14.6986332Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:10:14.6986645Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:10:14.6986942Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:10:14.6987215Z 2025-03-04T21:10:14.6987638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:14.6988380Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:10:14.6988699Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:10:14.6988990Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:10:14.6989254Z 2025-03-04T21:10:14.6989759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:14.6990288Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:14.6990627Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:10:14.6990871Z 2025-03-04T21:10:14.6991275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:14.6991803Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:14.6992146Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:10:14.6992385Z 2025-03-04T21:10:14.6992773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:14.6993277Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:14.6993599Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:10:14.6993837Z 2025-03-04T21:10:14.6994230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:14.6994768Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:10:14.6995115Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:10:14.6995348Z 2025-03-04T21:10:14.6995776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:14.6996306Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:10:14.6996568Z 2025-03-04T21:10:14.6996989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:14.6997512Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:10:14.6997769Z 2025-03-04T21:10:14.6998197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:14.6998732Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:10:14.6999048Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:10:14.6999380Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:10:14.6999769Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:10:14.7000061Z 2025-03-04T21:10:14.7000503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:14.7001048Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:10:14.7001394Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:10:14.7001727Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:10:14.7002083Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:10:14.7002346Z 2025-03-04T21:10:14.7002792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:14.7003304Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:10:14.7003635Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:10:14.7003987Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:10:14.7004245Z 2025-03-04T21:10:14.7004664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:14.7005171Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:10:14.7005499Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:10:14.7005849Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:10:14.7006104Z 2025-03-04T21:10:14.7006509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:14.7006987Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:10:14.7007255Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:10:14.7007494Z 2025-03-04T21:10:14.7007894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:14.7008358Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:10:14.7008619Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:10:14.7008856Z 2025-03-04T21:10:14.7009250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:14.7009739Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:10:14.7010043Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:10:14.7010298Z 2025-03-04T21:10:14.7010693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:14.7011173Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:10:14.7011472Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:10:14.7011727Z 2025-03-04T21:10:14.7012166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:14.7012782Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:10:14.7013108Z 2025-03-04T21:10:14.7013535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:14.7014088Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:10:14.7014404Z 2025-03-04T21:10:14.7014976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:14.7015622Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:10:14.7015934Z 2025-03-04T21:10:14.7016655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:10:14.7017403Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:10:14.7017679Z 2025-03-04T21:10:14.7018086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7018609Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:10:14.7018890Z 2025-03-04T21:10:14.7019444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:14.7020082Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:10:14.7020374Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:10:14.7020664Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:10:14.7020914Z 2025-03-04T21:10:14.7021497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:14.7022184Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:14.7022632Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:10:14.7022999Z 2025-03-04T21:10:14.7023577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:14.7024289Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:14.7024593Z 2025-03-04T21:10:14.7024998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7025497Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:10:14.7025756Z 2025-03-04T21:10:14.7026306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:14.7026910Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:10:14.7027193Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:10:14.7027495Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:10:14.7027740Z 2025-03-04T21:10:14.7028310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:14.7028955Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:14.7029404Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:10:14.7029760Z 2025-03-04T21:10:14.7030298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:14.7030994Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:14.7031289Z 2025-03-04T21:10:14.7031675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7032157Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:10:14.7032405Z 2025-03-04T21:10:14.7032932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:14.7033538Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:10:14.7033817Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:10:14.7034104Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:10:14.7034345Z 2025-03-04T21:10:14.7034898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:14.7035552Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:14.7035989Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:10:14.7036344Z 2025-03-04T21:10:14.7036891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:14.7037570Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:14.7037858Z 2025-03-04T21:10:14.7038248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7038732Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:10:14.7038982Z 2025-03-04T21:10:14.7039510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:14.7040117Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:10:14.7040393Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:10:14.7040674Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:10:14.7040913Z 2025-03-04T21:10:14.7041482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:14.7042142Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:14.7042570Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:10:14.7042944Z 2025-03-04T21:10:14.7043504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:14.7044202Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:14.7044495Z 2025-03-04T21:10:14.7044919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7045411Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:10:14.7045662Z 2025-03-04T21:10:14.7046185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:14.7046792Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:10:14.7047065Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:10:14.7047344Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:10:14.7047588Z 2025-03-04T21:10:14.7048135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:14.7048820Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:10:14.7049282Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:10:14.7049642Z 2025-03-04T21:10:14.7050188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:14.7050867Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:14.7051152Z 2025-03-04T21:10:14.7051535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7052015Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:10:14.7052266Z 2025-03-04T21:10:14.7052631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:14.7053351Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:10:14.7053846Z 2025-03-04T21:10:14.7054211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:14.7055105Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:10:14.7055762Z 2025-03-04T21:10:14.7056152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:14.7056709Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:10:14.7057031Z 2025-03-04T21:10:14.7057536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:10:14.7058154Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:10:14.7058440Z 2025-03-04T21:10:14.7058865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:10:14.7059400Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:10:14.7059685Z 2025-03-04T21:10:14.7060176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:10:14.7060775Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:10:14.7061044Z 2025-03-04T21:10:14.7061648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:10:14.7062362Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:10:14.7062694Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:10:14.7063047Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:10:14.7063412Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:10:14.7063683Z 2025-03-04T21:10:14.7064158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:10:14.7064731Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:10:14.7064983Z 2025-03-04T21:10:14.7065082Z 2025-03-04T21:10:14.7065182Z class GraphModule(torch.nn.Module): 2025-03-04T21:10:14.7067475Z def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:10:14.7069839Z l_features_p2_ = L_features_p2_ 2025-03-04T21:10:14.7070072Z l_features_p3_ = L_features_p3_ 2025-03-04T21:10:14.7070296Z l_features_p4_ = L_features_p4_ 2025-03-04T21:10:14.7070515Z l_features_p5_ = L_features_p5_ 2025-03-04T21:10:14.7070746Z l_features_p6_ = L_features_p6_ 2025-03-04T21:10:14.7071139Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:10:14.7071711Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:10:14.7072284Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:10:14.7072844Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:10:14.7073401Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:10:14.7073933Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:10:14.7074431Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:10:14.7074977Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:10:14.7075574Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:10:14.7076153Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:10:14.7076713Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:10:14.7077082Z 2025-03-04T21:10:14.7077649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.7078336Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:10:14.7078626Z 2025-03-04T21:10:14.7079039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7079556Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:10:14.7079833Z 2025-03-04T21:10:14.7080381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.7081071Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:10:14.7081357Z 2025-03-04T21:10:14.7081766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7082289Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:10:14.7082567Z 2025-03-04T21:10:14.7083062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:14.7083723Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:10:14.7084102Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:10:14.7084395Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:10:14.7084655Z 2025-03-04T21:10:14.7085131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:14.7085676Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:10:14.7085942Z 2025-03-04T21:10:14.7086380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:14.7086900Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:10:14.7087160Z 2025-03-04T21:10:14.7087633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:14.7088476Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:10:14.7088821Z 2025-03-04T21:10:14.7089359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:14.7089995Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:10:14.7090532Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:10:14.7091080Z add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:10:14.7091414Z x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:10:14.7091677Z 2025-03-04T21:10:14.7092260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.7092984Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:10:14.7093306Z 2025-03-04T21:10:14.7093738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7094312Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:10:14.7094682Z 2025-03-04T21:10:14.7095312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.7096031Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:10:14.7096333Z 2025-03-04T21:10:14.7096760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7097309Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:10:14.7097601Z 2025-03-04T21:10:14.7098123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:14.7098881Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:10:14.7099307Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:10:14.7099626Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:10:14.7099901Z 2025-03-04T21:10:14.7100425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:14.7101002Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:10:14.7101280Z 2025-03-04T21:10:14.7101742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:14.7102309Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:10:14.7102612Z 2025-03-04T21:10:14.7103142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:14.7103852Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:10:14.7104207Z 2025-03-04T21:10:14.7104739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:14.7105373Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:10:14.7105912Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:10:14.7106433Z add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:10:14.7106755Z x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4); add_1 = None 2025-03-04T21:10:14.7107006Z 2025-03-04T21:10:14.7107560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.7108234Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:10:14.7108519Z 2025-03-04T21:10:14.7108927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7109448Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:10:14.7109728Z 2025-03-04T21:10:14.7110278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.7110947Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:10:14.7111229Z 2025-03-04T21:10:14.7111632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7112149Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:10:14.7112424Z 2025-03-04T21:10:14.7112910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:14.7113569Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:10:14.7113942Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:10:14.7114218Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:10:14.7114464Z 2025-03-04T21:10:14.7114903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:14.7115566Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:10:14.7115819Z 2025-03-04T21:10:14.7116242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:14.7116755Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:10:14.7117038Z 2025-03-04T21:10:14.7117525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:14.7118192Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:10:14.7118532Z 2025-03-04T21:10:14.7119047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:14.7119653Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:10:14.7120163Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:10:14.7120658Z add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:10:14.7120962Z x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4); add_2 = None 2025-03-04T21:10:14.7121205Z 2025-03-04T21:10:14.7121729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.7122379Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:10:14.7122653Z 2025-03-04T21:10:14.7123038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7123544Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:10:14.7123812Z 2025-03-04T21:10:14.7124346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.7124996Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:10:14.7125265Z 2025-03-04T21:10:14.7125656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7126155Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:10:14.7126421Z 2025-03-04T21:10:14.7126813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:14.7127028Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:10:14.7127158Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:10:14.7127281Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:10:14.7127353Z 2025-03-04T21:10:14.7127698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:14.7127832Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:10:14.7127897Z 2025-03-04T21:10:14.7128225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:14.7128346Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:10:14.7128437Z 2025-03-04T21:10:14.7128814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:14.7129035Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:10:14.7129107Z 2025-03-04T21:10:14.7129516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:14.7129652Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:10:14.7129958Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:10:14.7130090Z add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:10:14.7130203Z x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:10:14.7130275Z 2025-03-04T21:10:14.7130703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.7130854Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:10:14.7130919Z 2025-03-04T21:10:14.7131217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7131356Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:10:14.7131429Z 2025-03-04T21:10:14.7131850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:10:14.7132001Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:10:14.7132066Z 2025-03-04T21:10:14.7132364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7132499Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:10:14.7132570Z 2025-03-04T21:10:14.7132937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:10:14.7133152Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:10:14.7133283Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:10:14.7133411Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:10:14.7133476Z 2025-03-04T21:10:14.7133905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:10:14.7134030Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:10:14.7134104Z 2025-03-04T21:10:14.7134458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:10:14.7134655Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:10:14.7134755Z 2025-03-04T21:10:14.7135189Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:10:14.7135426Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:10:14.7135506Z 2025-03-04T21:10:14.7135958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:10:14.7136100Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:10:14.7136430Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:10:14.7136555Z add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:10:14.7136679Z x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:10:14.7136745Z 2025-03-04T21:10:14.7137057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:10:14.7137184Z tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:10:14.7137322Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32); x_1 = None 2025-03-04T21:10:14.7137444Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32); x_2 = None 2025-03-04T21:10:14.7137574Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32); x_3 = None 2025-03-04T21:10:14.7137693Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32); x_4 = None 2025-03-04T21:10:14.7137767Z 2025-03-04T21:10:14.7138028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:14.7138466Z x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p2_ = None 2025-03-04T21:10:14.7138535Z 2025-03-04T21:10:14.7138819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:14.7139015Z x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False); x_5 = None 2025-03-04T21:10:14.7139087Z 2025-03-04T21:10:14.7139472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:14.7139925Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:14.7139992Z 2025-03-04T21:10:14.7140376Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:14.7140785Z x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_6 = None 2025-03-04T21:10:14.7140858Z 2025-03-04T21:10:14.7141142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:14.7141551Z x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p3_ = None 2025-03-04T21:10:14.7141622Z 2025-03-04T21:10:14.7141899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:14.7142104Z x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False); x_7 = None 2025-03-04T21:10:14.7142168Z 2025-03-04T21:10:14.7142543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:14.7142945Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:14.7143020Z 2025-03-04T21:10:14.7143377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:14.7143796Z x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_8 = None 2025-03-04T21:10:14.7143858Z 2025-03-04T21:10:14.7144113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:14.7144507Z x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p4_ = None 2025-03-04T21:10:14.7144581Z 2025-03-04T21:10:14.7144853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:14.7145041Z x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False); x_9 = None 2025-03-04T21:10:14.7145106Z 2025-03-04T21:10:14.7145485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:14.7145885Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:14.7145982Z 2025-03-04T21:10:14.7146334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:14.7146734Z x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_10 = None 2025-03-04T21:10:14.7146807Z 2025-03-04T21:10:14.7147061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:14.7147476Z x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p5_ = None 2025-03-04T21:10:14.7147544Z 2025-03-04T21:10:14.7147834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:14.7148008Z x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False); x_11 = None 2025-03-04T21:10:14.7148080Z 2025-03-04T21:10:14.7148445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:14.7148852Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:10:14.7148931Z 2025-03-04T21:10:14.7149280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:14.7149663Z x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_12 = None 2025-03-04T21:10:14.7149728Z 2025-03-04T21:10:14.7149981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:10:14.7150544Z x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:10:14.7150620Z 2025-03-04T21:10:14.7150884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:10:14.7151064Z x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False); x_13 = None 2025-03-04T21:10:14.7151130Z 2025-03-04T21:10:14.7151504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:10:14.7152121Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:10:14.7152241Z 2025-03-04T21:10:14.7152587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:10:14.7153184Z x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:10:14.7153256Z 2025-03-04T21:10:14.7153587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:10:14.7153774Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:10:14.7153918Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:10:14.7154086Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:10:14.7154231Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:10:14.7154391Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:10:14.7154527Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:10:14.7154679Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:10:14.7154811Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:10:14.7154965Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:10:14.7155096Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:10:14.7155170Z 2025-03-04T21:10:14.7155590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:10:14.7155773Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304); x_15 = None 2025-03-04T21:10:14.7155955Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:10:14.7156141Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:10:14.7156302Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152); x_16 = None 2025-03-04T21:10:14.7156496Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:10:14.7156673Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:10:14.7156821Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76); x_17 = None 2025-03-04T21:10:14.7156989Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:10:14.7157152Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:10:14.7157300Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38); x_18 = None 2025-03-04T21:10:14.7157489Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:10:14.7157677Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:10:14.7157816Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19); x_19 = None 2025-03-04T21:10:14.7157996Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:10:14.7158161Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:10:14.7158232Z 2025-03-04T21:10:14.7158631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:14.7158856Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:10:14.7158920Z 2025-03-04T21:10:14.7159356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:14.7159511Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:10:14.7159667Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:10:14.7159807Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:10:14.7159882Z 2025-03-04T21:10:14.7160251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:14.7160433Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:10:14.7160498Z 2025-03-04T21:10:14.7160812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:14.7160955Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:10:14.7161028Z 2025-03-04T21:10:14.7161335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:14.7161475Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:10:14.7161599Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:14.7161757Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:10:14.7161822Z 2025-03-04T21:10:14.7162141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:14.7162273Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:10:14.7162392Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:10:14.7162551Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:10:14.7162614Z 2025-03-04T21:10:14.7163049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:14.7163170Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:14.7163283Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:10:14.7163413Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:10:14.7163499Z 2025-03-04T21:10:14.7163813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:14.7163969Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:10:14.7164077Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:10:14.7164221Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:10:14.7164287Z 2025-03-04T21:10:14.7164618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:14.7164779Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:14.7164940Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:10:14.7165005Z 2025-03-04T21:10:14.7165323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:14.7165474Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:14.7165595Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:10:14.7165657Z 2025-03-04T21:10:14.7165964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:14.7166117Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:14.7166237Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:10:14.7166305Z 2025-03-04T21:10:14.7166617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:14.7166805Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:10:14.7166926Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:10:14.7166991Z 2025-03-04T21:10:14.7167339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:14.7167483Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:10:14.7167555Z 2025-03-04T21:10:14.7167894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:14.7168042Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:10:14.7168118Z 2025-03-04T21:10:14.7168465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:14.7168615Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:10:14.7168742Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:10:14.7168902Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:10:14.7169043Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:10:14.7169115Z 2025-03-04T21:10:14.7169489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:14.7169655Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:10:14.7169780Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:10:14.7169939Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:10:14.7170094Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:10:14.7170167Z 2025-03-04T21:10:14.7170500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:14.7170630Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:10:14.7170812Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:10:14.7170958Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:10:14.7171023Z 2025-03-04T21:10:14.7171364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:14.7171484Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:10:14.7171658Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:10:14.7171795Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:10:14.7171869Z 2025-03-04T21:10:14.7172183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:14.7172291Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:10:14.7172418Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:10:14.7172490Z 2025-03-04T21:10:14.7172799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:14.7172904Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:10:14.7173023Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:10:14.7173096Z 2025-03-04T21:10:14.7173398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:14.7173523Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:10:14.7173657Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:10:14.7173729Z 2025-03-04T21:10:14.7174031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:14.7174154Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:10:14.7174283Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:10:14.7174358Z 2025-03-04T21:10:14.7174773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:14.7174983Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:10:14.7175053Z 2025-03-04T21:10:14.7175446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:14.7175640Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:10:14.7175721Z 2025-03-04T21:10:14.7176124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:14.7176340Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:10:14.7176411Z 2025-03-04T21:10:14.7176849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:14.7177096Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:10:14.7177164Z 2025-03-04T21:10:14.7177620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:14.7177778Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:10:14.7177942Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:10:14.7178086Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:10:14.7178159Z 2025-03-04T21:10:14.7178540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:14.7178723Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:10:14.7178791Z 2025-03-04T21:10:14.7179119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:14.7179273Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:10:14.7179347Z 2025-03-04T21:10:14.7179667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:14.7179811Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:10:14.7179944Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:10:14.7180105Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:10:14.7180174Z 2025-03-04T21:10:14.7180509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:14.7180640Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:10:14.7180775Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:10:14.7180934Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:10:14.7181008Z 2025-03-04T21:10:14.7181331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:14.7181462Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:10:14.7181576Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:10:14.7181718Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:10:14.7181799Z 2025-03-04T21:10:14.7182121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:14.7182272Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:10:14.7182391Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:10:14.7182524Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:10:14.7182598Z 2025-03-04T21:10:14.7182901Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:14.7183068Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:14.7183206Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:10:14.7183281Z 2025-03-04T21:10:14.7183584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:14.7183748Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:14.7183873Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:10:14.7183939Z 2025-03-04T21:10:14.7184241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:14.7184395Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:14.7184520Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:10:14.7184585Z 2025-03-04T21:10:14.7184894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:14.7185080Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:10:14.7185200Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:10:14.7185268Z 2025-03-04T21:10:14.7185612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:14.7185755Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:10:14.7185828Z 2025-03-04T21:10:14.7186159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:14.7186308Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:10:14.7186373Z 2025-03-04T21:10:14.7186722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:14.7186863Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:10:14.7186998Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:10:14.7187152Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:10:14.7187303Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:10:14.7187388Z 2025-03-04T21:10:14.7187745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:14.7187898Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:10:14.7188030Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:10:14.7188410Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:10:14.7188570Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:10:14.7188638Z 2025-03-04T21:10:14.7188983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:14.7189132Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:10:14.7189313Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:10:14.7189456Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:10:14.7189535Z 2025-03-04T21:10:14.7189880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:14.7190010Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:10:14.7190193Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:10:14.7190334Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:10:14.7190414Z 2025-03-04T21:10:14.7190738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:14.7190854Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:10:14.7190982Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:10:14.7191059Z 2025-03-04T21:10:14.7191380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:14.7191489Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:10:14.7191611Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:10:14.7191687Z 2025-03-04T21:10:14.7192011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:14.7192146Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:10:14.7192287Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:10:14.7192362Z 2025-03-04T21:10:14.7192676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:14.7192805Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:10:14.7192941Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:10:14.7193018Z 2025-03-04T21:10:14.7193377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:14.7193585Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:10:14.7193682Z 2025-03-04T21:10:14.7194036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:14.7194232Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:10:14.7194304Z 2025-03-04T21:10:14.7194714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:14.7194907Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:10:14.7194973Z 2025-03-04T21:10:14.7195408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:14.7195626Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:10:14.7195704Z 2025-03-04T21:10:14.7196148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:14.7196317Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:10:14.7196477Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:10:14.7196628Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:10:14.7196696Z 2025-03-04T21:10:14.7197088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:14.7197262Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:10:14.7197339Z 2025-03-04T21:10:14.7197659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:14.7197817Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:10:14.7197891Z 2025-03-04T21:10:14.7198212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:14.7198355Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:10:14.7198486Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:10:14.7198649Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:10:14.7198719Z 2025-03-04T21:10:14.7199050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:14.7199180Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:10:14.7199312Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:10:14.7199470Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:10:14.7199547Z 2025-03-04T21:10:14.7199865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:14.7200025Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:10:14.7200119Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:10:14.7200281Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:10:14.7200350Z 2025-03-04T21:10:14.7200674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:14.7200852Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:10:14.7200961Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:10:14.7201095Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:10:14.7201168Z 2025-03-04T21:10:14.7201482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:14.7201668Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:14.7201792Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:10:14.7201869Z 2025-03-04T21:10:14.7202179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:14.7202344Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:14.7202460Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:10:14.7202536Z 2025-03-04T21:10:14.7202842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:14.7203007Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:14.7203123Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:10:14.7203198Z 2025-03-04T21:10:14.7203511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:14.7203708Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:10:14.7203822Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:10:14.7203898Z 2025-03-04T21:10:14.7204242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:14.7204394Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:10:14.7204470Z 2025-03-04T21:10:14.7204817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:14.7204967Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:10:14.7205035Z 2025-03-04T21:10:14.7205398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:14.7205536Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:10:14.7205681Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:10:14.7205838Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:10:14.7206008Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:10:14.7206073Z 2025-03-04T21:10:14.7206433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:14.7206592Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:10:14.7206741Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:10:14.7206901Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:10:14.7207049Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:10:14.7207118Z 2025-03-04T21:10:14.7207488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:14.7207612Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:10:14.7207785Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:10:14.7207922Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:10:14.7207993Z 2025-03-04T21:10:14.7208334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:14.7208459Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:10:14.7208628Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:10:14.7208771Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:10:14.7208840Z 2025-03-04T21:10:14.7209168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:14.7209274Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:10:14.7209403Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:10:14.7209470Z 2025-03-04T21:10:14.7209793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:14.7209891Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:10:14.7210017Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:10:14.7210084Z 2025-03-04T21:10:14.7210402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:14.7210523Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:10:14.7210670Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:10:14.7210738Z 2025-03-04T21:10:14.7211057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:14.7211176Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:10:14.7211319Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:10:14.7211386Z 2025-03-04T21:10:14.7211749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:14.7211969Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:10:14.7212044Z 2025-03-04T21:10:14.7212403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:14.7212577Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:10:14.7212650Z 2025-03-04T21:10:14.7213767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:14.7213968Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:10:14.7214035Z 2025-03-04T21:10:14.7214568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:14.7214825Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:10:14.7214909Z 2025-03-04T21:10:14.7215395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:14.7215574Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:10:14.7215735Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:10:14.7215892Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:10:14.7215965Z 2025-03-04T21:10:14.7216366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:14.7216547Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:10:14.7216635Z 2025-03-04T21:10:14.7216955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:14.7217116Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:10:14.7217186Z 2025-03-04T21:10:14.7217517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:14.7217652Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:10:14.7217789Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:10:14.7217937Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:10:14.7218012Z 2025-03-04T21:10:14.7218326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:14.7218460Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:10:14.7218580Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:10:14.7218739Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:10:14.7218803Z 2025-03-04T21:10:14.7219120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:14.7219269Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:10:14.7219388Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:10:14.7219521Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:10:14.7219596Z 2025-03-04T21:10:14.7219933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:14.7220091Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:10:14.7220186Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:10:14.7220324Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:10:14.7220389Z 2025-03-04T21:10:14.7220727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:14.7220896Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:14.7221013Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:10:14.7221086Z 2025-03-04T21:10:14.7221389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:14.7221549Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:14.7221664Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:10:14.7221736Z 2025-03-04T21:10:14.7222032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:14.7222193Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:14.7222304Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:10:14.7222374Z 2025-03-04T21:10:14.7222679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:14.7222871Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:10:14.7222983Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:10:14.7223053Z 2025-03-04T21:10:14.7223393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:14.7223540Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:10:14.7223608Z 2025-03-04T21:10:14.7223948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:14.7224088Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:10:14.7224160Z 2025-03-04T21:10:14.7224509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:14.7224652Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:10:14.7224779Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:10:14.7224938Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:10:14.7225108Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:10:14.7225217Z 2025-03-04T21:10:14.7225558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:14.7225705Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:10:14.7225848Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:10:14.7226009Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:10:14.7226146Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:10:14.7226217Z 2025-03-04T21:10:14.7226565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:14.7226689Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:10:14.7226861Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:10:14.7226997Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:10:14.7227069Z 2025-03-04T21:10:14.7227404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:14.7227526Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:10:14.7227690Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:10:14.7227830Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:10:14.7227897Z 2025-03-04T21:10:14.7228215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:14.7228317Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:10:14.7228444Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:10:14.7228510Z 2025-03-04T21:10:14.7228826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:14.7228922Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:10:14.7229048Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:10:14.7229114Z 2025-03-04T21:10:14.7229429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:14.7229547Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:10:14.7229690Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:10:14.7229756Z 2025-03-04T21:10:14.7230065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:14.7230188Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:10:14.7230322Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:10:14.7230386Z 2025-03-04T21:10:14.7230732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:14.7230940Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:10:14.7231116Z 2025-03-04T21:10:14.7231438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:14.7231621Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:10:14.7231686Z 2025-03-04T21:10:14.7232062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:14.7232230Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:10:14.7232304Z 2025-03-04T21:10:14.7232708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:14.7232925Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:10:14.7232990Z 2025-03-04T21:10:14.7233426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:14.7233575Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:10:14.7233732Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:10:14.7233865Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:10:14.7233943Z 2025-03-04T21:10:14.7234309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:14.7234485Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:10:14.7234559Z 2025-03-04T21:10:14.7234865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:14.7235017Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:10:14.7235082Z 2025-03-04T21:10:14.7235396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:14.7235529Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:10:14.7235661Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:10:14.7235809Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:10:14.7235881Z 2025-03-04T21:10:14.7236190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:14.7236320Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:10:14.7236442Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:10:14.7236595Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:10:14.7236660Z 2025-03-04T21:10:14.7236973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:14.7237106Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:10:14.7237216Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:10:14.7237344Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:10:14.7237416Z 2025-03-04T21:10:14.7237734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:14.7237886Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:10:14.7237975Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:10:14.7238108Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:10:14.7238172Z 2025-03-04T21:10:14.7238490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:14.7238645Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:14.7238764Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:10:14.7238827Z 2025-03-04T21:10:14.7239124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:14.7239269Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:14.7239385Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:10:14.7239450Z 2025-03-04T21:10:14.7239744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:14.7239891Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:14.7240007Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:10:14.7240069Z 2025-03-04T21:10:14.7240368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:14.7240556Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:10:14.7240664Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:10:14.7240736Z 2025-03-04T21:10:14.7241065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:14.7241210Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:10:14.7241273Z 2025-03-04T21:10:14.7241605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:14.7241738Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:10:14.7241808Z 2025-03-04T21:10:14.7242143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:14.7242283Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:10:14.7242405Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:10:14.7242587Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:10:14.7242724Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:10:14.7242813Z 2025-03-04T21:10:14.7243158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:14.7243319Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:10:14.7243439Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:10:14.7243592Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:10:14.7243724Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:10:14.7243796Z 2025-03-04T21:10:14.7244139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:14.7244262Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:10:14.7244418Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:10:14.7244553Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:10:14.7244619Z 2025-03-04T21:10:14.7244951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:14.7245062Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:10:14.7245230Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:10:14.7245360Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:10:14.7245432Z 2025-03-04T21:10:14.7245736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:14.7245840Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:10:14.7245952Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:10:14.7246027Z 2025-03-04T21:10:14.7246326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:14.7246426Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:10:14.7246539Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:10:14.7246610Z 2025-03-04T21:10:14.7246913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:14.7247033Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:10:14.7247161Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:10:14.7247233Z 2025-03-04T21:10:14.7247531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:14.7247650Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:10:14.7247775Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:10:14.7247847Z 2025-03-04T21:10:14.7248186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:14.7248422Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:10:14.7248501Z 2025-03-04T21:10:14.7248834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:14.7249013Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:10:14.7249078Z 2025-03-04T21:10:14.7249456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:14.7249622Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:10:14.7249694Z 2025-03-04T21:10:14.7250179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:10:14.7250325Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:10:14.7250389Z 2025-03-04T21:10:14.7250689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7250828Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:10:14.7250902Z 2025-03-04T21:10:14.7251324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:14.7251445Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:10:14.7251549Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:10:14.7251674Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:10:14.7251738Z 2025-03-04T21:10:14.7252197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:14.7252327Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:14.7252559Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:10:14.7252622Z 2025-03-04T21:10:14.7253078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:14.7253249Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:14.7253322Z 2025-03-04T21:10:14.7253614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7253744Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:10:14.7253809Z 2025-03-04T21:10:14.7254243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:14.7254361Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:10:14.7254497Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:10:14.7254693Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:10:14.7254798Z 2025-03-04T21:10:14.7255285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:14.7255459Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:14.7255730Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:10:14.7255805Z 2025-03-04T21:10:14.7256347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:14.7256528Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:14.7256607Z 2025-03-04T21:10:14.7256928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7257064Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:10:14.7257131Z 2025-03-04T21:10:14.7257570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:14.7257685Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:10:14.7257799Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:10:14.7257921Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:10:14.7257994Z 2025-03-04T21:10:14.7258447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:14.7258588Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:14.7258828Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:10:14.7258901Z 2025-03-04T21:10:14.7259353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:14.7259531Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:14.7259595Z 2025-03-04T21:10:14.7259897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7260024Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:10:14.7260098Z 2025-03-04T21:10:14.7260532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:14.7260654Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:10:14.7260759Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:10:14.7260883Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:10:14.7260966Z 2025-03-04T21:10:14.7261423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:14.7261573Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:14.7261833Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:10:14.7261906Z 2025-03-04T21:10:14.7262369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:14.7262542Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:14.7262625Z 2025-03-04T21:10:14.7262923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7263048Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:10:14.7263122Z 2025-03-04T21:10:14.7263551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:14.7263671Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:10:14.7263777Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:10:14.7263898Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:10:14.7263965Z 2025-03-04T21:10:14.7264423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:14.7264589Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:10:14.7264832Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:10:14.7264899Z 2025-03-04T21:10:14.7265357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:14.7265518Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:14.7265595Z 2025-03-04T21:10:14.7265887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:14.7266019Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:10:14.7266084Z 2025-03-04T21:10:14.7266370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:14.7266752Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:10:14.7266826Z 2025-03-04T21:10:14.7267105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:14.7267595Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:10:14.7267687Z 2025-03-04T21:10:14.7267966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:14.7268187Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:10:14.7268253Z 2025-03-04T21:10:14.7268644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:10:14.7268788Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:10:14.7268888Z 2025-03-04T21:10:14.7269188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:10:14.7269342Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:10:14.7269406Z 2025-03-04T21:10:14.7269798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:10:14.7269931Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:10:14.7270004Z 2025-03-04T21:10:14.7270485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:10:14.7270631Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:10:14.7270752Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:10:14.7270914Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:10:14.7271047Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:10:14.7271121Z 2025-03-04T21:10:14.7271493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:10:14.7271616Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:10:14.7271681Z 2025-03-04T21:10:16.0376648Z 2025-03-04T21:10:16.0377700Z class GraphModule(torch.nn.Module): 2025-03-04T21:10:16.0379523Z def forward(self, L_pred_anchor_deltas_0_: "f32[4, 269952, 4][1079808, 4, 1]cpu", L_anchors_0_tensor: "f32[269952, 4][4, 1]cpu", L_pred_anchor_deltas_1_: "f32[4, 67488, 4][269952, 4, 1]cpu", L_anchors_1_tensor: "f32[67488, 4][4, 1]cpu", L_pred_anchor_deltas_2_: "f32[4, 16872, 4][67488, 4, 1]cpu", L_anchors_2_tensor: "f32[16872, 4][4, 1]cpu", L_pred_anchor_deltas_3_: "f32[4, 4218, 4][16872, 4, 1]cpu", L_anchors_3_tensor: "f32[4218, 4][4, 1]cpu", L_pred_anchor_deltas_4_: "f32[4, 1083, 4][4332, 4, 1]cpu", L_anchors_4_tensor: "f32[1083, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 269952][269952, 1]cpu", L_pred_objectness_logits_1_: "f32[4, 67488][67488, 1]cpu", L_pred_objectness_logits_2_: "f32[4, 16872][16872, 1]cpu", L_pred_objectness_logits_3_: "f32[4, 4218][4218, 1]cpu", L_pred_objectness_logits_4_: "f32[4, 1083][1083, 1]cpu"): 2025-03-04T21:10:16.0381135Z l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_ 2025-03-04T21:10:16.0381408Z l_anchors_0_tensor = L_anchors_0_tensor 2025-03-04T21:10:16.0381995Z l_pred_anchor_deltas_1_ = L_pred_anchor_deltas_1_ 2025-03-04T21:10:16.0382264Z l_anchors_1_tensor = L_anchors_1_tensor 2025-03-04T21:10:16.0382570Z l_pred_anchor_deltas_2_ = L_pred_anchor_deltas_2_ 2025-03-04T21:10:16.0382828Z l_anchors_2_tensor = L_anchors_2_tensor 2025-03-04T21:10:16.0383095Z l_pred_anchor_deltas_3_ = L_pred_anchor_deltas_3_ 2025-03-04T21:10:16.0383361Z l_anchors_3_tensor = L_anchors_3_tensor 2025-03-04T21:10:16.0383674Z l_pred_anchor_deltas_4_ = L_pred_anchor_deltas_4_ 2025-03-04T21:10:16.0383933Z l_anchors_4_tensor = L_anchors_4_tensor 2025-03-04T21:10:16.0384211Z l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_ 2025-03-04T21:10:16.0384604Z l_pred_objectness_logits_1_ = L_pred_objectness_logits_1_ 2025-03-04T21:10:16.0384909Z l_pred_objectness_logits_2_ = L_pred_objectness_logits_2_ 2025-03-04T21:10:16.0385204Z l_pred_objectness_logits_3_ = L_pred_objectness_logits_3_ 2025-03-04T21:10:16.0385548Z l_pred_objectness_logits_4_ = L_pred_objectness_logits_4_ 2025-03-04T21:10:16.0385804Z 2025-03-04T21:10:16.0386387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:16.0387122Z pred_anchor_deltas_i: "f32[1079808, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4); l_pred_anchor_deltas_0_ = None 2025-03-04T21:10:16.0387481Z 2025-03-04T21:10:16.0388046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:16.0389022Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0); l_anchors_0_tensor = None 2025-03-04T21:10:16.0389454Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:10:16.0389828Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:10:16.0390117Z 2025-03-04T21:10:16.0390685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:16.0391329Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.float(); pred_anchor_deltas_i = None 2025-03-04T21:10:16.0391633Z 2025-03-04T21:10:16.0392070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:16.0392624Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:10:16.0392921Z 2025-03-04T21:10:16.0393331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:16.0393843Z getitem: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:10:16.0394163Z getitem_1: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:16.0394496Z widths: "f32[1079808][1]cpu" = getitem - getitem_1; getitem = getitem_1 = None 2025-03-04T21:10:16.0394764Z 2025-03-04T21:10:16.0395181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:16.0395693Z getitem_2: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:10:16.0396001Z getitem_3: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:10:16.0396333Z heights: "f32[1079808][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:10:16.0396650Z 2025-03-04T21:10:16.0397056Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:16.0397581Z getitem_4: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:16.0397851Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:10:16.0398126Z ctr_x: "f32[1079808][1]cpu" = getitem_4 + mul; getitem_4 = mul = None 2025-03-04T21:10:16.0398450Z 2025-03-04T21:10:16.0398877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:16.0399418Z getitem_5: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:10:16.0399736Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:10:16.0400034Z ctr_y: "f32[1079808][1]cpu" = getitem_5 + mul_1; getitem_5 = mul_1 = None 2025-03-04T21:10:16.0400294Z 2025-03-04T21:10:16.0400724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:16.0401243Z getitem_6: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:16.0401573Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_6 / 1.0; getitem_6 = None 2025-03-04T21:10:16.0401812Z 2025-03-04T21:10:16.0402207Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:16.0402714Z getitem_7: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:16.0403040Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_7 / 1.0; getitem_7 = None 2025-03-04T21:10:16.0403280Z 2025-03-04T21:10:16.0403673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:16.0404188Z getitem_8: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:16.0404516Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:10:16.0404754Z 2025-03-04T21:10:16.0405150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:16.0405697Z getitem_9: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:10:16.0406054Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:10:16.0406289Z 2025-03-04T21:10:16.0406718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:16.0407259Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:10:16.0407526Z 2025-03-04T21:10:16.0407948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:16.0408484Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:10:16.0408744Z 2025-03-04T21:10:16.0409179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:16.0409740Z getitem_10: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:10:16.0410097Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_10; dx = getitem_10 = None 2025-03-04T21:10:16.0410451Z getitem_11: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:10:16.0410838Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_11; mul_2 = getitem_11 = None 2025-03-04T21:10:16.0411106Z 2025-03-04T21:10:16.0411568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:16.0412132Z getitem_12: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:10:16.0412474Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_12; dy = getitem_12 = None 2025-03-04T21:10:16.0412830Z getitem_13: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:10:16.0413203Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_13; mul_3 = getitem_13 = None 2025-03-04T21:10:16.0413495Z 2025-03-04T21:10:16.0413944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:16.0414481Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:10:16.0414938Z getitem_14: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:10:16.0415317Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_14; exp = getitem_14 = None 2025-03-04T21:10:16.0415593Z 2025-03-04T21:10:16.0416051Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:16.0416587Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:10:16.0416954Z getitem_15: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:10:16.0417321Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_15; exp_1 = getitem_15 = None 2025-03-04T21:10:16.0417595Z 2025-03-04T21:10:16.0418016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:16.0418502Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:10:16.0418781Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:10:16.0419028Z 2025-03-04T21:10:16.0419441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:16.0419914Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:10:16.0420189Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:10:16.0420439Z 2025-03-04T21:10:16.0420849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:16.0421348Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:10:16.0421658Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:10:16.0421919Z 2025-03-04T21:10:16.0422332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:16.0422821Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:10:16.0423131Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:10:16.0423390Z 2025-03-04T21:10:16.0423842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:16.0424497Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:10:16.0424805Z 2025-03-04T21:10:16.0425244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:16.0425838Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:10:16.0426137Z 2025-03-04T21:10:16.0426625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:16.0427272Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:10:16.0427583Z 2025-03-04T21:10:16.0428093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:16.0428792Z pred_anchor_deltas_i_1: "f32[269952, 4][4, 1]cpu" = l_pred_anchor_deltas_1_.reshape(-1, 4); l_pred_anchor_deltas_1_ = None 2025-03-04T21:10:16.0429130Z 2025-03-04T21:10:16.0429666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:16.0430374Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = l_anchors_1_tensor.unsqueeze(0); l_anchors_1_tensor = None 2025-03-04T21:10:16.0430784Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:10:16.0431152Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:10:16.0431434Z 2025-03-04T21:10:16.0431900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:16.0432502Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:10:16.0432798Z 2025-03-04T21:10:16.0433201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:16.0433726Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:10:16.0433996Z 2025-03-04T21:10:16.0434404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:16.0434916Z getitem_16: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:10:16.0435238Z getitem_17: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:10:16.0435577Z widths_1: "f32[269952][1]cpu" = getitem_16 - getitem_17; getitem_16 = getitem_17 = None 2025-03-04T21:10:16.0435849Z 2025-03-04T21:10:16.0436259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:16.0436765Z getitem_18: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:10:16.0437073Z getitem_19: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:10:16.0437401Z heights_1: "f32[269952][1]cpu" = getitem_18 - getitem_19; getitem_18 = getitem_19 = None 2025-03-04T21:10:16.0437694Z 2025-03-04T21:10:16.0438093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:16.0438603Z getitem_20: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:10:16.0438914Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:10:16.0439198Z ctr_x_1: "f32[269952][1]cpu" = getitem_20 + mul_10; getitem_20 = mul_10 = None 2025-03-04T21:10:16.0439474Z 2025-03-04T21:10:16.0439882Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:16.0440399Z getitem_21: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:10:16.0440703Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:10:16.0441004Z ctr_y_1: "f32[269952][1]cpu" = getitem_21 + mul_11; getitem_21 = mul_11 = None 2025-03-04T21:10:16.0441261Z 2025-03-04T21:10:16.0441665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:16.0442181Z getitem_22: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:16.0442515Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_22 / 1.0; getitem_22 = None 2025-03-04T21:10:16.0442759Z 2025-03-04T21:10:16.0443148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:16.0443655Z getitem_23: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:16.0443983Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_23 / 1.0; getitem_23 = None 2025-03-04T21:10:16.0444227Z 2025-03-04T21:10:16.0444619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:16.0445125Z getitem_24: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:16.0445447Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_24 / 1.0; getitem_24 = None 2025-03-04T21:10:16.0445684Z 2025-03-04T21:10:16.0446075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:16.0446616Z getitem_25: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:10:16.0446969Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_25 / 1.0; getitem_25 = None 2025-03-04T21:10:16.0447306Z 2025-03-04T21:10:16.0447754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:16.0448292Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:10:16.0448555Z 2025-03-04T21:10:16.0448980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:16.0449512Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:10:16.0449778Z 2025-03-04T21:10:16.0450211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:16.0450758Z getitem_26: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:10:16.0451109Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_26; dx_1 = getitem_26 = None 2025-03-04T21:10:16.0451457Z getitem_27: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:10:16.0451859Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_27; mul_12 = getitem_27 = None 2025-03-04T21:10:16.0452132Z 2025-03-04T21:10:16.0452605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:16.0453166Z getitem_28: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:10:16.0453502Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_28; dy_1 = getitem_28 = None 2025-03-04T21:10:16.0453852Z getitem_29: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:10:16.0454239Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_29; mul_13 = getitem_29 = None 2025-03-04T21:10:16.0455359Z 2025-03-04T21:10:16.0455951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:16.0456519Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:10:16.0456901Z getitem_30: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:10:16.0457276Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_30; exp_2 = getitem_30 = None 2025-03-04T21:10:16.0457554Z 2025-03-04T21:10:16.0458001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:16.0458526Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:10:16.0458889Z getitem_31: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:10:16.0459265Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_31; exp_3 = getitem_31 = None 2025-03-04T21:10:16.0459532Z 2025-03-04T21:10:16.0459958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:16.0460439Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:10:16.0460723Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:10:16.0460976Z 2025-03-04T21:10:16.0461390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:16.0461868Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:10:16.0462149Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:10:16.0462400Z 2025-03-04T21:10:16.0462808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:16.0463298Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:10:16.0463617Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:10:16.0463882Z 2025-03-04T21:10:16.0464290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:16.0464787Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:10:16.0465101Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:10:16.0465419Z 2025-03-04T21:10:16.0465872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:16.0466509Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:10:16.0466831Z 2025-03-04T21:10:16.0467287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:16.0467860Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:10:16.0468162Z 2025-03-04T21:10:16.0468667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:16.0469303Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:10:16.0469618Z 2025-03-04T21:10:16.0470122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:16.0470794Z pred_anchor_deltas_i_2: "f32[67488, 4][4, 1]cpu" = l_pred_anchor_deltas_2_.reshape(-1, 4); l_pred_anchor_deltas_2_ = None 2025-03-04T21:10:16.0471130Z 2025-03-04T21:10:16.0471654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:16.0472330Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = l_anchors_2_tensor.unsqueeze(0); l_anchors_2_tensor = None 2025-03-04T21:10:16.0472730Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:10:16.0473086Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:10:16.0473352Z 2025-03-04T21:10:16.0473817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:16.0474418Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_2.float(); pred_anchor_deltas_i_2 = None 2025-03-04T21:10:16.0474713Z 2025-03-04T21:10:16.0475117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:16.0475636Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:10:16.0475911Z 2025-03-04T21:10:16.0476315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:16.0476819Z getitem_32: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:10:16.0477135Z getitem_33: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:10:16.0477473Z widths_2: "f32[67488][1]cpu" = getitem_32 - getitem_33; getitem_32 = getitem_33 = None 2025-03-04T21:10:16.0477742Z 2025-03-04T21:10:16.0478163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:16.0478665Z getitem_34: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:10:16.0478977Z getitem_35: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:10:16.0479350Z heights_2: "f32[67488][1]cpu" = getitem_34 - getitem_35; getitem_34 = getitem_35 = None 2025-03-04T21:10:16.0479652Z 2025-03-04T21:10:16.0480074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:16.0480567Z getitem_36: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:10:16.0480857Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:10:16.0481142Z ctr_x_2: "f32[67488][1]cpu" = getitem_36 + mul_20; getitem_36 = mul_20 = None 2025-03-04T21:10:16.0481401Z 2025-03-04T21:10:16.0481803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:16.0482324Z getitem_37: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:10:16.0482648Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:10:16.0482926Z ctr_y_2: "f32[67488][1]cpu" = getitem_37 + mul_21; getitem_37 = mul_21 = None 2025-03-04T21:10:16.0483180Z 2025-03-04T21:10:16.0483591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:16.0484101Z getitem_38: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:16.0484433Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_38 / 1.0; getitem_38 = None 2025-03-04T21:10:16.0484672Z 2025-03-04T21:10:16.0485064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:16.0485569Z getitem_39: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:16.0485897Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_39 / 1.0; getitem_39 = None 2025-03-04T21:10:16.0486137Z 2025-03-04T21:10:16.0486528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:16.0487032Z getitem_40: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:16.0487356Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_40 / 1.0; getitem_40 = None 2025-03-04T21:10:16.0487594Z 2025-03-04T21:10:16.0487982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:16.0488708Z getitem_41: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:10:16.0489069Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_41 / 1.0; getitem_41 = None 2025-03-04T21:10:16.0489315Z 2025-03-04T21:10:16.0489747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:16.0490352Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:10:16.0490624Z 2025-03-04T21:10:16.0491047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:16.0491584Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:10:16.0491852Z 2025-03-04T21:10:16.0492295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:16.0493977Z getitem_42: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:10:16.0494314Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_42; dx_2 = getitem_42 = None 2025-03-04T21:10:16.0494771Z getitem_43: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:10:16.0495177Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_43; mul_22 = getitem_43 = None 2025-03-04T21:10:16.0495491Z 2025-03-04T21:10:16.0496013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:16.0496596Z getitem_44: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:10:16.0496942Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_44; dy_2 = getitem_44 = None 2025-03-04T21:10:16.0497317Z getitem_45: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:10:16.0497684Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_45; mul_23 = getitem_45 = None 2025-03-04T21:10:16.0497958Z 2025-03-04T21:10:16.0498386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:16.0498911Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:10:16.0499253Z getitem_46: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:10:16.0499617Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_46; exp_4 = getitem_46 = None 2025-03-04T21:10:16.0499887Z 2025-03-04T21:10:16.0500321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:16.0500835Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:10:16.0501182Z getitem_47: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:10:16.0501550Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_47; exp_5 = getitem_47 = None 2025-03-04T21:10:16.0501817Z 2025-03-04T21:10:16.0502230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:16.0502712Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:10:16.0502992Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:10:16.0503244Z 2025-03-04T21:10:16.0503653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:16.0504131Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:10:16.0504407Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:10:16.0504657Z 2025-03-04T21:10:16.0505063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:16.0505556Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:10:16.0505870Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:10:16.0506135Z 2025-03-04T21:10:16.0506543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:16.0507034Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:10:16.0507401Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:10:16.0507681Z 2025-03-04T21:10:16.0508136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:16.0508730Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:10:16.0509053Z 2025-03-04T21:10:16.0509486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:16.0510040Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:10:16.0510329Z 2025-03-04T21:10:16.0510820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:16.0511432Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:10:16.0511726Z 2025-03-04T21:10:16.0512212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:16.0512876Z pred_anchor_deltas_i_3: "f32[16872, 4][4, 1]cpu" = l_pred_anchor_deltas_3_.reshape(-1, 4); l_pred_anchor_deltas_3_ = None 2025-03-04T21:10:16.0513201Z 2025-03-04T21:10:16.0513716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:16.0514384Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = l_anchors_3_tensor.unsqueeze(0); l_anchors_3_tensor = None 2025-03-04T21:10:16.0514783Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:10:16.0515131Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:10:16.0515393Z 2025-03-04T21:10:16.0515860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:16.0516462Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:10:16.0516754Z 2025-03-04T21:10:16.0517155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:16.0517673Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:10:16.0517944Z 2025-03-04T21:10:16.0518346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:16.0518851Z getitem_48: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:10:16.0519165Z getitem_49: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:10:16.0519501Z widths_3: "f32[16872][1]cpu" = getitem_48 - getitem_49; getitem_48 = getitem_49 = None 2025-03-04T21:10:16.0519765Z 2025-03-04T21:10:16.0520173Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:16.0520672Z getitem_50: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:10:16.0521009Z getitem_51: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:10:16.0521348Z heights_3: "f32[16872][1]cpu" = getitem_50 - getitem_51; getitem_50 = getitem_51 = None 2025-03-04T21:10:16.0521648Z 2025-03-04T21:10:16.0522049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:16.0522558Z getitem_52: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:10:16.0522835Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:10:16.0523114Z ctr_x_3: "f32[16872][1]cpu" = getitem_52 + mul_30; getitem_52 = mul_30 = None 2025-03-04T21:10:16.0523370Z 2025-03-04T21:10:16.0523776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:16.0524313Z getitem_53: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:10:16.0524618Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:10:16.0524900Z ctr_y_3: "f32[16872][1]cpu" = getitem_53 + mul_31; getitem_53 = mul_31 = None 2025-03-04T21:10:16.0525154Z 2025-03-04T21:10:16.0525558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:16.0526075Z getitem_54: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:16.0526408Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_54 / 1.0; getitem_54 = None 2025-03-04T21:10:16.0526650Z 2025-03-04T21:10:16.0527042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:16.0527553Z getitem_55: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:16.0527878Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_55 / 1.0; getitem_55 = None 2025-03-04T21:10:16.0528121Z 2025-03-04T21:10:16.0528509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:16.0529022Z getitem_56: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:16.0529348Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_56 / 1.0; getitem_56 = None 2025-03-04T21:10:16.0529586Z 2025-03-04T21:10:16.0529978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:16.0530523Z getitem_57: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:10:16.0530878Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_57 / 1.0; getitem_57 = None 2025-03-04T21:10:16.0531115Z 2025-03-04T21:10:16.0531544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:16.0532081Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:10:16.0532346Z 2025-03-04T21:10:16.0532768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:16.0533297Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:10:16.0533558Z 2025-03-04T21:10:16.0533991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:16.0534706Z getitem_58: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:10:16.0535109Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_58; dx_3 = getitem_58 = None 2025-03-04T21:10:16.0535492Z getitem_59: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:10:16.0536035Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_59; mul_32 = getitem_59 = None 2025-03-04T21:10:16.0536314Z 2025-03-04T21:10:16.0536818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:16.0537434Z getitem_60: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:10:16.0537814Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_60; dy_3 = getitem_60 = None 2025-03-04T21:10:16.0538195Z getitem_61: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:10:16.0538588Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_61; mul_33 = getitem_61 = None 2025-03-04T21:10:16.0538884Z 2025-03-04T21:10:16.0539374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:16.0539953Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:10:16.0540327Z getitem_62: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:10:16.0540725Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_62; exp_6 = getitem_62 = None 2025-03-04T21:10:16.0541019Z 2025-03-04T21:10:16.0541494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:16.0542047Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:10:16.0542392Z getitem_63: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:10:16.0542758Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_63; exp_7 = getitem_63 = None 2025-03-04T21:10:16.0543027Z 2025-03-04T21:10:16.0543439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:16.0546602Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:10:16.0546909Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:10:16.0547166Z 2025-03-04T21:10:16.0547600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:16.0548084Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:10:16.0548360Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:10:16.0548611Z 2025-03-04T21:10:16.0549022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:16.0549519Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:10:16.0549835Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:10:16.0550100Z 2025-03-04T21:10:16.0550505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:16.0551083Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:10:16.0551392Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:10:16.0551677Z 2025-03-04T21:10:16.0552129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:16.0552768Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:10:16.0553093Z 2025-03-04T21:10:16.0553524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:16.0554088Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:10:16.0554384Z 2025-03-04T21:10:16.0554902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:16.0555538Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:10:16.0555839Z 2025-03-04T21:10:16.0556347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:10:16.0557030Z pred_anchor_deltas_i_4: "f32[4332, 4][4, 1]cpu" = l_pred_anchor_deltas_4_.reshape(-1, 4); l_pred_anchor_deltas_4_ = None 2025-03-04T21:10:16.0557357Z 2025-03-04T21:10:16.0557880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:10:16.0558555Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = l_anchors_4_tensor.unsqueeze(0); l_anchors_4_tensor = None 2025-03-04T21:10:16.0558952Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:10:16.0559299Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:10:16.0559563Z 2025-03-04T21:10:16.0560030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:16.0560628Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_4.float(); pred_anchor_deltas_i_4 = None 2025-03-04T21:10:16.0560919Z 2025-03-04T21:10:16.0561320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:16.0561836Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:10:16.0562108Z 2025-03-04T21:10:16.0562513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:16.0563013Z getitem_64: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:10:16.0563326Z getitem_65: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:10:16.0563648Z widths_4: "f32[4332][1]cpu" = getitem_64 - getitem_65; getitem_64 = getitem_65 = None 2025-03-04T21:10:16.0563918Z 2025-03-04T21:10:16.0564328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:16.0564860Z getitem_66: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:10:16.0565169Z getitem_67: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:10:16.0565526Z heights_4: "f32[4332][1]cpu" = getitem_66 - getitem_67; getitem_66 = getitem_67 = None 2025-03-04T21:10:16.0565797Z 2025-03-04T21:10:16.0566220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:16.0566713Z getitem_68: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:10:16.0566983Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:10:16.0567261Z ctr_x_4: "f32[4332][1]cpu" = getitem_68 + mul_40; getitem_68 = mul_40 = None 2025-03-04T21:10:16.0567513Z 2025-03-04T21:10:16.0567936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:16.0568452Z getitem_69: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:10:16.0568747Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:10:16.0569020Z ctr_y_4: "f32[4332][1]cpu" = getitem_69 + mul_41; getitem_69 = mul_41 = None 2025-03-04T21:10:16.0569268Z 2025-03-04T21:10:16.0569679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:16.0570185Z getitem_70: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:16.0570509Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_70 / 1.0; getitem_70 = None 2025-03-04T21:10:16.0570746Z 2025-03-04T21:10:16.0571145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:16.0571665Z getitem_71: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:16.0572000Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_71 / 1.0; getitem_71 = None 2025-03-04T21:10:16.0572242Z 2025-03-04T21:10:16.0572643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:16.0573166Z getitem_72: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:16.0573494Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_72 / 1.0; getitem_72 = None 2025-03-04T21:10:16.0573734Z 2025-03-04T21:10:16.0574136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:16.0574792Z getitem_73: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:10:16.0575192Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_73 / 1.0; getitem_73 = None 2025-03-04T21:10:16.0575464Z 2025-03-04T21:10:16.0575926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:16.0576494Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:10:16.0576781Z 2025-03-04T21:10:16.0577222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:16.0577769Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:10:16.0578038Z 2025-03-04T21:10:16.0578514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:16.0579085Z getitem_74: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:10:16.0579411Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_74; dx_4 = getitem_74 = None 2025-03-04T21:10:16.0579772Z getitem_75: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:10:16.0580148Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_75; mul_42 = getitem_75 = None 2025-03-04T21:10:16.0580419Z 2025-03-04T21:10:16.0580868Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:16.0581421Z getitem_76: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:10:16.0581769Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_76; dy_4 = getitem_76 = None 2025-03-04T21:10:16.0582111Z getitem_77: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:10:16.0582465Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_77; mul_43 = getitem_77 = None 2025-03-04T21:10:16.0582732Z 2025-03-04T21:10:16.0583177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:16.0583709Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:10:16.0584053Z getitem_78: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:10:16.0584420Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_78; exp_8 = getitem_78 = None 2025-03-04T21:10:16.0584691Z 2025-03-04T21:10:16.0585138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:16.0585669Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:10:16.0586014Z getitem_79: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:10:16.0586385Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_79; exp_9 = getitem_79 = None 2025-03-04T21:10:16.0586647Z 2025-03-04T21:10:16.0587072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:16.0587565Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:10:16.0587841Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:10:16.0588233Z 2025-03-04T21:10:16.0588666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:16.0589155Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:10:16.0589429Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:10:16.0589674Z 2025-03-04T21:10:16.0590091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:16.0590644Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:10:16.0590961Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:10:16.0591251Z 2025-03-04T21:10:16.0591679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:16.0592255Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:10:16.0592604Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:10:16.0592868Z 2025-03-04T21:10:16.0593317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:16.0593959Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:10:16.0594277Z 2025-03-04T21:10:16.0594715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:16.0595284Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:10:16.0595605Z 2025-03-04T21:10:16.0596095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:10:16.0596702Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:10:16.0596999Z 2025-03-04T21:10:16.0597574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:10:16.0598282Z arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:10:16.0598536Z 2025-03-04T21:10:16.0598926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:16.0599417Z batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:10:16.0599677Z 2025-03-04T21:10:16.0600202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:16.0600889Z topk = l_pred_objectness_logits_0_.topk(1000, dim = 1); l_pred_objectness_logits_0_ = None 2025-03-04T21:10:16.0601231Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:10:16.0601508Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:10:16.0601739Z 2025-03-04T21:10:16.0602302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:16.0602954Z getitem_82: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:16.0603382Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_82, topk_idx)]; proposals_i_5 = getitem_82 = topk_idx = None 2025-03-04T21:10:16.0603733Z 2025-03-04T21:10:16.0604282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:16.0604956Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:16.0605241Z 2025-03-04T21:10:16.0605621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:16.0606120Z to_6: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:10:16.0606359Z 2025-03-04T21:10:16.0606885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:16.0607573Z topk_1 = l_pred_objectness_logits_1_.topk(1000, dim = 1); l_pred_objectness_logits_1_ = None 2025-03-04T21:10:16.0607956Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:10:16.0608267Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:10:16.0608511Z 2025-03-04T21:10:16.0609067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:16.0609737Z getitem_86: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:16.0610171Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_86, topk_idx_1)]; proposals_i_6 = getitem_86 = topk_idx_1 = None 2025-03-04T21:10:16.0610530Z 2025-03-04T21:10:16.0611074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:16.0611750Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:16.0612039Z 2025-03-04T21:10:16.0612427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:16.0612907Z to_7: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:10:16.0613160Z 2025-03-04T21:10:16.0613696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:16.0614371Z topk_2 = l_pred_objectness_logits_2_.topk(1000, dim = 1); l_pred_objectness_logits_2_ = None 2025-03-04T21:10:16.0614791Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:10:16.0615093Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:10:16.0615352Z 2025-03-04T21:10:16.0615946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:16.0616656Z getitem_90: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:16.0617106Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_90, topk_idx_2)]; proposals_i_7 = getitem_90 = topk_idx_2 = None 2025-03-04T21:10:16.0617486Z 2025-03-04T21:10:16.0618060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:16.0618774Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:16.0619078Z 2025-03-04T21:10:16.0619480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:16.0619984Z to_8: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:10:16.0620267Z 2025-03-04T21:10:16.0620802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:16.0621495Z topk_3 = l_pred_objectness_logits_3_.topk(1000, dim = 1); l_pred_objectness_logits_3_ = None 2025-03-04T21:10:16.0621840Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:10:16.0622145Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:10:16.0622393Z 2025-03-04T21:10:16.0622959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:16.0623620Z getitem_94: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:10:16.0624076Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_94, topk_idx_3)]; proposals_i_8 = getitem_94 = topk_idx_3 = None 2025-03-04T21:10:16.0624441Z 2025-03-04T21:10:16.0624991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:16.0625683Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:16.0625974Z 2025-03-04T21:10:16.0626366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:16.0626849Z to_9: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:10:16.0627101Z 2025-03-04T21:10:16.0627639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:10:16.0628289Z topk_4 = l_pred_objectness_logits_4_.topk(1000, dim = 1); l_pred_objectness_logits_4_ = None 2025-03-04T21:10:16.0628625Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:10:16.0628902Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:10:16.0629144Z 2025-03-04T21:10:16.0629686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:10:16.0630354Z getitem_98: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:10:16.0630807Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_98, topk_idx_4)]; proposals_i_9 = getitem_98 = topk_idx_4 = None 2025-03-04T21:10:16.0631160Z 2025-03-04T21:10:16.0631699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:10:16.0632368Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:10:16.0632654Z 2025-03-04T21:10:16.0633036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:10:16.0633514Z to_10: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:10:16.0633763Z 2025-03-04T21:10:16.0634129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:16.0634869Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:10:16.0635379Z 2025-03-04T21:10:16.0635765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:16.0636566Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:10:16.0637148Z 2025-03-04T21:10:16.0637527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:16.0638054Z level_ids: "i64[5000][1]cpu" = torch.cat([to_6, to_7, to_8, to_9, to_10], 0); to_6 = to_7 = to_8 = to_9 = to_10 = level_ids = None 2025-03-04T21:10:16.0638368Z 2025-03-04T21:10:16.0638843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:10:16.0639430Z getitem_100: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:10:16.0639695Z 2025-03-04T21:10:16.0640088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:10:16.0640595Z tensor: "f32[5000, 4][4, 1]cpu" = getitem_100.to(torch.float32); getitem_100 = None 2025-03-04T21:10:16.0640866Z 2025-03-04T21:10:16.0641340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:10:16.0641911Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:10:16.0642168Z 2025-03-04T21:10:16.0642745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:10:16.0643415Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor); tensor = None 2025-03-04T21:10:16.0643726Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:10:16.0644057Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:10:16.0644403Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:10:16.0644659Z 2025-03-04T21:10:16.0645122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:10:16.0645667Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:10:16.0645896Z 2025-03-04T21:10:22.5560775Z 2025-03-04T21:10:22.5564546Z class GraphModule(torch.nn.Module): 2025-03-04T21:10:22.5570338Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:10:22.5572769Z l_stack0_ = L_stack0_ 2025-03-04T21:10:22.5573148Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:10:22.5574114Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:10:22.5575087Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:10:22.5575683Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:10:22.5576242Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:10:22.5576825Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:10:22.5577410Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:10:22.5577990Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:10:22.5578487Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:10:22.5578910Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:10:22.5579331Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:10:22.5579725Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:10:22.5580024Z 2025-03-04T21:10:22.5580430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:10:22.5581386Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:10:22.5582117Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:10:22.5582851Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:10:22.5583580Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:10:22.5584300Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:10:22.5584593Z 2025-03-04T21:10:22.5585009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:10:22.5586944Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:10:22.5587974Z 2025-03-04T21:10:22.5588637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:10:22.5589702Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:10:22.5590481Z 2025-03-04T21:10:22.5590908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:10:22.5591390Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:22.5591657Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:10:22.5591902Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:10:22.5592187Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:22.5592441Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:10:22.5592686Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:10:22.5592966Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:22.5593217Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:10:22.5593454Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:10:22.5593728Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:22.5593979Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:10:22.5594216Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:10:22.5594437Z 2025-03-04T21:10:22.5594818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:22.5595608Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:10:22.5596169Z 2025-03-04T21:10:22.5596640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:22.5597219Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:10:22.5597492Z 2025-03-04T21:10:22.5597893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:22.5598429Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:10:22.5598718Z 2025-03-04T21:10:22.5599124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:22.5599637Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:10:22.5599963Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:22.5600287Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:10:22.5600554Z 2025-03-04T21:10:22.5600957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:22.5601482Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:10:22.5601821Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:10:22.5608809Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:10:22.5609134Z 2025-03-04T21:10:22.5609723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:22.5610261Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:22.5610556Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:10:22.5610844Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:10:22.5611113Z 2025-03-04T21:10:22.5611573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:22.5612104Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:10:22.5612417Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:10:22.5612706Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:10:22.5612963Z 2025-03-04T21:10:22.5613395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:22.5613917Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:22.5614251Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:10:22.5614513Z 2025-03-04T21:10:22.5615116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:22.5615696Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:22.5616060Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:10:22.5616305Z 2025-03-04T21:10:22.5616707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:22.5617229Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:22.5617560Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:10:22.5617803Z 2025-03-04T21:10:22.5618211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:22.5618770Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:10:22.5619131Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:10:22.5619374Z 2025-03-04T21:10:22.5619816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:22.5620366Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:10:22.5620632Z 2025-03-04T21:10:22.5621066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:22.5621644Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:10:22.5621906Z 2025-03-04T21:10:22.5622354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:22.5622953Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:10:22.5623323Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:10:22.5623672Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:10:22.5624025Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:10:22.5624290Z 2025-03-04T21:10:22.5624757Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:22.5625303Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:10:22.5625626Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:10:22.5625963Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:10:22.5626353Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:10:22.5626614Z 2025-03-04T21:10:22.5627033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:22.5627540Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:10:22.5627874Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:10:22.5628220Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:10:22.5628470Z 2025-03-04T21:10:22.5628892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:22.5629392Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:10:22.5629730Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:10:22.5630083Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:10:22.5630339Z 2025-03-04T21:10:22.5630734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:22.5631213Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:10:22.5631472Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:10:22.5631710Z 2025-03-04T21:10:22.5632109Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:22.5632565Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:10:22.5632828Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:10:22.5633064Z 2025-03-04T21:10:22.5633459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:22.5633939Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:10:22.5634231Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:10:22.5634511Z 2025-03-04T21:10:22.5634905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:22.5635397Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:10:22.5635689Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:10:22.5635938Z 2025-03-04T21:10:22.5636392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:22.5636982Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:10:22.5637383Z 2025-03-04T21:10:22.5638052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:22.5638621Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:10:22.5638914Z 2025-03-04T21:10:22.5639533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:10:22.5640371Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:10:22.5640797Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:10:22.5641087Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:10:22.5641390Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:10:22.5641701Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:10:22.5641969Z 2025-03-04T21:10:22.5642370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:10:22.5642940Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:10:22.5643292Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:10:22.5643544Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:10:22.5643919Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:10:22.5644274Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:10:22.5644536Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:10:22.5644898Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:10:22.5645246Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:10:22.5645486Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:10:22.5645851Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:10:22.5646199Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:10:22.5646436Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:10:22.5646668Z 2025-03-04T21:10:22.5647100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:10:22.5647667Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:10:22.5647965Z 2025-03-04T21:10:22.5648431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:10:22.5649271Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:10:22.5649765Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:10:22.5650060Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:10:22.5650364Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:10:22.5650693Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:10:22.5650963Z 2025-03-04T21:10:22.5651539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:10:22.5652297Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:10:22.5652650Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:10:22.5652994Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:10:22.5653339Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:10:22.5653637Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:10:22.5653888Z 2025-03-04T21:10:22.5654346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:10:22.5654989Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:10:22.5655244Z 2025-03-04T21:10:22.5655409Z 2025-03-04T21:10:22.5655509Z class GraphModule(torch.nn.Module): 2025-03-04T21:10:22.5657464Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:10:22.5659521Z l_stack0_ = L_stack0_ 2025-03-04T21:10:22.5659888Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:10:22.5660386Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:10:22.5660883Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:10:22.5661372Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:10:22.5661911Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:10:22.5662502Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:10:22.5663123Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:10:22.5663732Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:10:22.5664239Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:10:22.5664683Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:10:22.5665107Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:10:22.5665524Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:10:22.5665832Z 2025-03-04T21:10:22.5666244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:10:22.5666749Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:10:22.5667499Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:10:22.5668265Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:10:22.5669032Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:10:22.5669789Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:10:22.5670089Z 2025-03-04T21:10:22.5670516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:10:22.5671535Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:10:22.5672249Z 2025-03-04T21:10:22.5672666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:10:22.5673665Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:10:22.5674402Z 2025-03-04T21:10:22.5674784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:10:22.5675252Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:22.5675509Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:10:22.5675748Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:10:22.5676024Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:22.5676278Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:10:22.5676519Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:10:22.5676818Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:22.5677069Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:10:22.5677340Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:10:22.5677609Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:22.5677853Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:10:22.5678082Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:10:22.5678300Z 2025-03-04T21:10:22.5678691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:22.5679470Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:10:22.5680026Z 2025-03-04T21:10:22.5680507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:22.5681090Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:10:22.5681363Z 2025-03-04T21:10:22.5681763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:22.5682298Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:10:22.5682582Z 2025-03-04T21:10:22.5682977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:22.5683507Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:10:22.5683853Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:22.5684186Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:10:22.5684459Z 2025-03-04T21:10:22.5684866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:22.5685370Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:10:22.5685692Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:10:22.5686039Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:10:22.5686315Z 2025-03-04T21:10:22.5686716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:22.5687215Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:22.5687504Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:10:22.5687780Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:10:22.5688029Z 2025-03-04T21:10:22.5688687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:22.5689215Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:10:22.5689528Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:10:22.5689821Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:10:22.5690197Z 2025-03-04T21:10:22.5690632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:22.5691207Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:22.5691553Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:10:22.5691810Z 2025-03-04T21:10:22.5692253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:22.5692787Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:22.5693131Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:10:22.5693378Z 2025-03-04T21:10:22.5693812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:22.5694347Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:22.5694788Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:10:22.5695061Z 2025-03-04T21:10:22.5695510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:22.5696121Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:10:22.5696516Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:10:22.5696783Z 2025-03-04T21:10:22.5697266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:22.5697856Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:10:22.5698136Z 2025-03-04T21:10:22.5698582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:22.5699141Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:10:22.5699430Z 2025-03-04T21:10:22.5699915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:22.5700531Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:10:22.5700895Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:10:22.5701269Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:10:22.5701639Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:10:22.5701912Z 2025-03-04T21:10:22.5702381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:22.5702967Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:10:22.5703310Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:10:22.5703663Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:10:22.5704030Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:10:22.5704385Z 2025-03-04T21:10:22.5704829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:22.5705398Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:10:22.5705745Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:10:22.5706131Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:10:22.5706400Z 2025-03-04T21:10:22.5706825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:22.5707335Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:10:22.5707694Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:10:22.5708049Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:10:22.5708307Z 2025-03-04T21:10:22.5708713Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:22.5709185Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:10:22.5709455Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:10:22.5709696Z 2025-03-04T21:10:22.5710092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:22.5710550Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:10:22.5710813Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:10:22.5711046Z 2025-03-04T21:10:22.5711441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:22.5711916Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:10:22.5712212Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:10:22.5712456Z 2025-03-04T21:10:22.5712846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:22.5713319Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:10:22.5713606Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:10:22.5713853Z 2025-03-04T21:10:22.5714289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:22.5714877Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:10:22.5715170Z 2025-03-04T21:10:22.5715590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:22.5716142Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:10:22.5716428Z 2025-03-04T21:10:22.5716874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:10:22.5717551Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:10:22.5718093Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:10:22.5718561Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:10:22.5718997Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:10:22.5719309Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:10:22.5719563Z 2025-03-04T21:10:22.5719972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:10:22.5720536Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:10:22.5720893Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:10:22.5721139Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:10:22.5721525Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:10:22.5721874Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:10:22.5722120Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:10:22.5722509Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:10:22.5722851Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:10:22.5723090Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:10:22.5723452Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:10:22.5723796Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:10:22.5724028Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:10:22.5724248Z 2025-03-04T21:10:22.5724671Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:10:22.5725239Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:10:22.5725533Z 2025-03-04T21:10:22.5725973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:10:22.5726649Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:10:22.5727068Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:10:22.5727354Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:10:22.5727648Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:10:22.5727955Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:10:22.5728208Z 2025-03-04T21:10:22.5728765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:10:22.5729463Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:10:22.5729800Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:10:22.5730131Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:10:22.5730469Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:10:22.5730757Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:10:22.5730997Z 2025-03-04T21:10:22.5731485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:10:22.5732029Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:10:22.5732268Z 2025-03-04T21:10:22.5732418Z 2025-03-04T21:10:22.5732508Z class GraphModule(torch.nn.Module): 2025-03-04T21:10:22.5734434Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:10:22.5736719Z l_stack0_ = L_stack0_ 2025-03-04T21:10:22.5737090Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:10:22.5737590Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:10:22.5738088Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:10:22.5738586Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:10:22.5739133Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:10:22.5739726Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:10:22.5740322Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:10:22.5740907Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:10:22.5741410Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:10:22.5741832Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:10:22.5742254Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:10:22.5742667Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:10:22.5742977Z 2025-03-04T21:10:22.5743370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:10:22.5743870Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:10:22.5744613Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:10:22.5745371Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:10:22.5746157Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:10:22.5746920Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:10:22.5747238Z 2025-03-04T21:10:22.5747668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:10:22.5748676Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:10:22.5749370Z 2025-03-04T21:10:22.5749782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:10:22.5750755Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:10:22.5751460Z 2025-03-04T21:10:22.5751821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:10:22.5752271Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:22.5752525Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:10:22.5752754Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:10:22.5753026Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:22.5753275Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:10:22.5753509Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:10:22.5753773Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:22.5754019Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:10:22.5754249Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:10:22.5754513Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:22.5754758Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:10:22.5754988Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:10:22.5755211Z 2025-03-04T21:10:22.5755586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:22.5756361Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:10:22.5756916Z 2025-03-04T21:10:22.5757380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:22.5757959Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:10:22.5758234Z 2025-03-04T21:10:22.5758629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:22.5759187Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:10:22.5759464Z 2025-03-04T21:10:22.5759890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:22.5760406Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:10:22.5760747Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:22.5761087Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:10:22.5761368Z 2025-03-04T21:10:22.5761766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:22.5762297Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:10:22.5762620Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:10:22.5762960Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:10:22.5763237Z 2025-03-04T21:10:22.5763637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:22.5764140Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:22.5764425Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:10:22.5764705Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:10:22.5764956Z 2025-03-04T21:10:22.5765365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:22.5765883Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:10:22.5766195Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:10:22.5766482Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:10:22.5766738Z 2025-03-04T21:10:22.5767147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:22.5767661Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:22.5767989Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:10:22.5768227Z 2025-03-04T21:10:22.5768616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:22.5769129Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:22.5769452Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:10:22.5769696Z 2025-03-04T21:10:22.5770104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:22.5770622Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:22.5770945Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:10:22.5771182Z 2025-03-04T21:10:22.5771575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:22.5772135Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:10:22.5772501Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:10:22.5772737Z 2025-03-04T21:10:22.5773171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:22.5773749Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:10:22.5774027Z 2025-03-04T21:10:22.5774475Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:22.5775114Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:10:22.5775402Z 2025-03-04T21:10:22.5775924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:22.5776530Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:10:22.5776877Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:10:22.5777237Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:10:22.5777614Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:10:22.5777889Z 2025-03-04T21:10:22.5778346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:22.5778911Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:10:22.5779251Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:10:22.5779603Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:10:22.5779974Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:10:22.5780230Z 2025-03-04T21:10:22.5780674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:22.5781203Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:10:22.5781537Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:10:22.5781888Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:10:22.5782146Z 2025-03-04T21:10:22.5782591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:22.5783128Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:10:22.5783481Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:10:22.5783856Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:10:22.5784125Z 2025-03-04T21:10:22.5784546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:22.5785031Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:10:22.5785309Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:10:22.5785580Z 2025-03-04T21:10:22.5785997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:22.5786499Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:10:22.5786774Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:10:22.5787020Z 2025-03-04T21:10:22.5787454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:22.5787959Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:10:22.5788476Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:10:22.5788746Z 2025-03-04T21:10:22.5789242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:22.5789745Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:10:22.5790059Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:10:22.5790315Z 2025-03-04T21:10:22.5790764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:22.5791368Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:10:22.5791683Z 2025-03-04T21:10:22.5792103Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:22.5792661Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:10:22.5792956Z 2025-03-04T21:10:22.5793403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:10:22.5794083Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:10:22.5794518Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:10:22.5794807Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:10:22.5795105Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:10:22.5795410Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:10:22.5795663Z 2025-03-04T21:10:22.5796046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:10:22.5796604Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:10:22.5796959Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:10:22.5797201Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:10:22.5797570Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:10:22.5797920Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:10:22.5798162Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:10:22.5798527Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:10:22.5798878Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:10:22.5799169Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:10:22.5799762Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:10:22.5800313Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:10:22.5800553Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:10:22.5800779Z 2025-03-04T21:10:22.5801237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:10:22.5801808Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:10:22.5802099Z 2025-03-04T21:10:22.5802541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:10:22.5803221Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:10:22.5803638Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:10:22.5803927Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:10:22.5804218Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:10:22.5804521Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:10:22.5804775Z 2025-03-04T21:10:22.5805327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:10:22.5806018Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:10:22.5806355Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:10:22.5806688Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:10:22.5807024Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:10:22.5807312Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:10:22.5807556Z 2025-03-04T21:10:22.5807998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:10:22.5808519Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:10:22.5808756Z 2025-03-04T21:10:22.5808900Z 2025-03-04T21:10:22.5808992Z class GraphModule(torch.nn.Module): 2025-03-04T21:10:22.5810859Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:10:22.5812840Z l_stack0_ = L_stack0_ 2025-03-04T21:10:22.5813217Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:10:22.5813720Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:10:22.5814212Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:10:22.5814821Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:10:22.5815423Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:10:22.5816079Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:10:22.5816725Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:10:22.5817356Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:10:22.5817895Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:10:22.5818349Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:10:22.5818795Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:10:22.5819244Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:10:22.5819578Z 2025-03-04T21:10:22.5819998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:10:22.5820520Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:10:22.5821274Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:10:22.5822039Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:10:22.5822813Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:10:22.5823572Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:10:22.5823878Z 2025-03-04T21:10:22.5824312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:10:22.5825337Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:10:22.5826096Z 2025-03-04T21:10:22.5826542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:10:22.5827611Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:10:22.5828424Z 2025-03-04T21:10:22.5828826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:10:22.5829316Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:22.5829589Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:10:22.5829871Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:10:22.5830171Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:22.5830444Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:10:22.5830703Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:10:22.5830993Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:22.5831260Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:10:22.5831534Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:10:22.5831817Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:22.5832081Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:10:22.5832327Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:10:22.5832549Z 2025-03-04T21:10:22.5832943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:22.5833754Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:10:22.5834335Z 2025-03-04T21:10:22.5834813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:22.5835426Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:10:22.5835707Z 2025-03-04T21:10:22.5836127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:22.5836685Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:10:22.5836984Z 2025-03-04T21:10:22.5837408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:22.5837940Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:10:22.5838275Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:22.5838610Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:10:22.5838881Z 2025-03-04T21:10:22.5839287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:22.5839791Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:10:22.5840104Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:10:22.5840440Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:10:22.5840714Z 2025-03-04T21:10:22.5841115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:22.5841632Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:22.5841916Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:10:22.5842214Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:10:22.5842465Z 2025-03-04T21:10:22.5842868Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:22.5843426Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:10:22.5843738Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:10:22.5844027Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:10:22.5844282Z 2025-03-04T21:10:22.5844703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:22.5845219Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:22.5845550Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:10:22.5845791Z 2025-03-04T21:10:22.5846185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:22.5846696Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:22.5847020Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:10:22.5847258Z 2025-03-04T21:10:22.5847647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:22.5848158Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:22.5848478Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:10:22.5848713Z 2025-03-04T21:10:22.5849102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:22.5849638Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:10:22.5849984Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:10:22.5850219Z 2025-03-04T21:10:22.5850644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:22.5851180Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:10:22.5851439Z 2025-03-04T21:10:22.5851862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:22.5852388Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:10:22.5852645Z 2025-03-04T21:10:22.5853078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:22.5853626Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:10:22.5853951Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:10:22.5854308Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:10:22.5854793Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:10:22.5855123Z 2025-03-04T21:10:22.5855626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:22.5856268Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:10:22.5856623Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:10:22.5856966Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:10:22.5857326Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:10:22.5857595Z 2025-03-04T21:10:22.5858049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:22.5858571Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:10:22.5858915Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:10:22.5859275Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:10:22.5859534Z 2025-03-04T21:10:22.5859970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:22.5860490Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:10:22.5860839Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:10:22.5861204Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:10:22.5861466Z 2025-03-04T21:10:22.5861879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:22.5862358Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:10:22.5862632Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:10:22.5862874Z 2025-03-04T21:10:22.5863285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:22.5863761Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:10:22.5864030Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:10:22.5864274Z 2025-03-04T21:10:22.5864682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:22.5865171Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:10:22.5865478Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:10:22.5865733Z 2025-03-04T21:10:22.5866135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:22.5866634Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:10:22.5866930Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:10:22.5867187Z 2025-03-04T21:10:22.5867635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:22.5868260Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:10:22.5868586Z 2025-03-04T21:10:22.5868997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:22.5869546Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:10:22.5869831Z 2025-03-04T21:10:22.5870290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:10:22.5870951Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:10:22.5871365Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:10:22.5871667Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:10:22.5871959Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:10:22.5872259Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:10:22.5872504Z 2025-03-04T21:10:22.5872871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:10:22.5873411Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:10:22.5873754Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:10:22.5873992Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:10:22.5874350Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:10:22.5874687Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:10:22.5874924Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:10:22.5875281Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:10:22.5875611Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:10:22.5875842Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:10:22.5876194Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:10:22.5876523Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:10:22.5876755Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:10:22.5876973Z 2025-03-04T21:10:22.5877384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:10:22.5877934Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:10:22.5878219Z 2025-03-04T21:10:22.5878651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:10:22.5879300Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:10:22.5879710Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:10:22.5879992Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:10:22.5880287Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:10:22.5880591Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:10:22.5880868Z 2025-03-04T21:10:22.5881418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:10:22.5882143Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:10:22.5882478Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:10:22.5882830Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:10:22.5883166Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:10:22.5883451Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:10:22.5883686Z 2025-03-04T21:10:22.5884140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:10:22.5884664Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:10:22.5884902Z 2025-03-04T21:10:23.1235067Z 2025-03-04T21:10:23.1235911Z class GraphModule(torch.nn.Module): 2025-03-04T21:10:23.1236917Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:10:23.1237747Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:10:23.1237984Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:10:23.1238313Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:10:23.1238743Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:10:23.1239156Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:10:23.1239556Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:10:23.1239862Z 2025-03-04T21:10:23.1240295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:10:23.1240785Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:23.1241049Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:10:23.1241290Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:10:23.1241578Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:23.1241840Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:10:23.1242085Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:10:23.1242366Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:23.1242619Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:10:23.1242855Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:10:23.1243129Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:23.1243374Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:10:23.1243608Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:10:23.1243833Z 2025-03-04T21:10:23.1244224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:23.1245025Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:10:23.1246399Z 2025-03-04T21:10:23.1246905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:23.1247567Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:10:23.1247844Z 2025-03-04T21:10:23.1248317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:23.1248866Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:10:23.1249158Z 2025-03-04T21:10:23.1249574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:23.1250138Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:10:23.1250470Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:23.1250808Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:10:23.1251087Z 2025-03-04T21:10:23.1251503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:23.1252021Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:10:23.1252342Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:10:23.1252685Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:10:23.1252969Z 2025-03-04T21:10:23.1253381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:23.1253885Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:23.1254173Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:10:23.1254489Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:10:23.1254863Z 2025-03-04T21:10:23.1255323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:23.1255923Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:10:23.1256270Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:10:23.1256573Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:10:23.1256839Z 2025-03-04T21:10:23.1257282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:23.1257817Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:23.1258157Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:10:23.1258404Z 2025-03-04T21:10:23.1258808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:23.1259329Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:23.1259663Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:10:23.1259936Z 2025-03-04T21:10:23.1260339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:23.1260880Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:23.1261211Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:10:23.1261450Z 2025-03-04T21:10:23.1261898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:23.1262460Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:10:23.1262820Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:10:23.1263061Z 2025-03-04T21:10:23.1263515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:23.1264065Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:10:23.1264331Z 2025-03-04T21:10:23.1264764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:23.1265305Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:10:23.1265567Z 2025-03-04T21:10:23.1266014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:23.1266602Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:10:23.1266944Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:10:23.1267292Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:10:23.1267658Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:10:23.1267927Z 2025-03-04T21:10:23.1268383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:23.1268945Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:10:23.1269277Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:10:23.1269618Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:10:23.1269977Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:10:23.1270242Z 2025-03-04T21:10:23.1270680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:23.1271268Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:10:23.1271615Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:10:23.1271973Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:10:23.1272230Z 2025-03-04T21:10:23.1272666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:23.1273168Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:10:23.1273535Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:10:23.1273912Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:10:23.1274163Z 2025-03-04T21:10:23.1274558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:23.1275050Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:10:23.1275320Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:10:23.1275559Z 2025-03-04T21:10:23.1275956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:23.1276418Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:10:23.1276699Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:10:23.1276941Z 2025-03-04T21:10:23.1277337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:23.1277809Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:10:23.1278105Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:10:23.1278349Z 2025-03-04T21:10:23.1278738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:23.1279207Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:10:23.1279495Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:10:23.1279743Z 2025-03-04T21:10:23.1280171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:23.1280769Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:10:23.1281049Z 2025-03-04T21:10:23.1281461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:23.1282012Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:10:23.1282301Z 2025-03-04T21:10:23.1282749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:10:23.1283432Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:10:23.1283861Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:10:23.1284150Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:10:23.1284449Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:10:23.1284758Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:10:23.1285014Z 2025-03-04T21:10:23.1285399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:10:23.1286659Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:10:23.1287080Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:10:23.1287383Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:10:23.1287761Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:10:23.1288324Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:10:23.1288581Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:10:23.1288951Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:10:23.1289382Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:10:23.1289641Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:10:23.1290008Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:10:23.1290349Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:10:23.1290591Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:10:23.1290845Z 2025-03-04T21:10:23.1291307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:10:23.1291930Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:10:23.1292272Z 2025-03-04T21:10:23.1292719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:10:23.1293385Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:10:23.1293800Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:10:23.1294087Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:10:23.1294399Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:10:23.1294842Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:10:23.1295136Z 2025-03-04T21:10:23.1295764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:10:23.1296480Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:10:23.1296822Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:10:23.1297158Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:10:23.1297502Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:10:23.1297795Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:10:23.1298029Z 2025-03-04T21:10:23.1298472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:10:23.1298999Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:10:23.1299236Z 2025-03-04T21:10:23.1313881Z 2025-03-04T21:10:23.1314251Z class GraphModule(torch.nn.Module): 2025-03-04T21:10:23.1315172Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:10:23.1316214Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:10:23.1316466Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:10:23.1316840Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:10:23.1317262Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:10:23.1317671Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:10:23.1318106Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:10:23.1319335Z 2025-03-04T21:10:23.1319851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:10:23.1320346Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:23.1320616Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:10:23.1321004Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:10:23.1321306Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:23.1321577Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:10:23.1321836Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:10:23.1322236Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:23.1322600Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:10:23.1322913Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:10:23.1323196Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:23.1323449Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:10:23.1323688Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:10:23.1323919Z 2025-03-04T21:10:23.1324410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:23.1325294Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:10:23.1325871Z 2025-03-04T21:10:23.1326348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:23.1326935Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:10:23.1327210Z 2025-03-04T21:10:23.1327612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:23.1328157Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:10:23.1328455Z 2025-03-04T21:10:23.1328870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:23.1329386Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:10:23.1329709Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:23.1330045Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:10:23.1330320Z 2025-03-04T21:10:23.1330746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:23.1331325Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:10:23.1331722Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:10:23.1332071Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:10:23.1332385Z 2025-03-04T21:10:23.1332837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:23.1333432Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:23.1333739Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:10:23.1334033Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:10:23.1334295Z 2025-03-04T21:10:23.1334844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:23.1335480Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:10:23.1335831Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:10:23.1336157Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:10:23.1336445Z 2025-03-04T21:10:23.1336922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:23.1337524Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:23.1337874Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:10:23.1338129Z 2025-03-04T21:10:23.1338546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:23.1339095Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:23.1339441Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:10:23.1339682Z 2025-03-04T21:10:23.1340095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:23.1340635Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:23.1340976Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:10:23.1341225Z 2025-03-04T21:10:23.1341644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:23.1342224Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:10:23.1342596Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:10:23.1342846Z 2025-03-04T21:10:23.1343354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:23.1343906Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:10:23.1344175Z 2025-03-04T21:10:23.1344613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:23.1345154Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:10:23.1345418Z 2025-03-04T21:10:23.1345870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:23.1346462Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:10:23.1346820Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:10:23.1347171Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:10:23.1347547Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:10:23.1347812Z 2025-03-04T21:10:23.1348266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:23.1348816Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:10:23.1349162Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:10:23.1349498Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:10:23.1349848Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:10:23.1350108Z 2025-03-04T21:10:23.1350540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:23.1351047Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:10:23.1351386Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:10:23.1351739Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:10:23.1351994Z 2025-03-04T21:10:23.1352417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:23.1352923Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:10:23.1353265Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:10:23.1353619Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:10:23.1353875Z 2025-03-04T21:10:23.1354283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:23.1354752Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:10:23.1355019Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:10:23.1355259Z 2025-03-04T21:10:23.1355662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:23.1356128Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:10:23.1356394Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:10:23.1356634Z 2025-03-04T21:10:23.1357034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:23.1357519Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:10:23.1357814Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:10:23.1358068Z 2025-03-04T21:10:23.1358464Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:23.1358965Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:10:23.1359255Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:10:23.1359551Z 2025-03-04T21:10:23.1359990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:23.1360589Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:10:23.1360886Z 2025-03-04T21:10:23.1361306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:23.1361865Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:10:23.1362162Z 2025-03-04T21:10:23.1362660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:10:23.1363345Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:10:23.1363773Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:10:23.1364068Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:10:23.1364371Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:10:23.1364674Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:10:23.1364928Z 2025-03-04T21:10:23.1365312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:10:23.1365879Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:10:23.1366236Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:10:23.1366484Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:10:23.1366854Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:10:23.1367203Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:10:23.1367450Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:10:23.1367817Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:10:23.1368157Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:10:23.1368396Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:10:23.1368763Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:10:23.1369108Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:10:23.1369365Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:10:23.1369588Z 2025-03-04T21:10:23.1370018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:10:23.1370619Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:10:23.1370952Z 2025-03-04T21:10:23.1371410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:10:23.1372084Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:10:23.1372527Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:10:23.1372839Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:10:23.1373143Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:10:23.1373467Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:10:23.1373729Z 2025-03-04T21:10:23.1374318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:10:23.1375193Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:10:23.1375598Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:10:23.1375998Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:10:23.1376389Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:10:23.1376700Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:10:23.1376956Z 2025-03-04T21:10:23.1377425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:10:23.1377983Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:10:23.1378234Z 2025-03-04T21:10:23.1404217Z 2025-03-04T21:10:23.1409256Z class GraphModule(torch.nn.Module): 2025-03-04T21:10:23.1410482Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:10:23.1411412Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:10:23.1411649Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:10:23.1411986Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:10:23.1412421Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:10:23.1412842Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:10:23.1413254Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:10:23.1413561Z 2025-03-04T21:10:23.1414000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:10:23.1414499Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:23.1414934Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:10:23.1415207Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:10:23.1416181Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:23.1416472Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:10:23.1416726Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:10:23.1417012Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:23.1417270Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:10:23.1417513Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:10:23.1417790Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:10:23.1418546Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:10:23.1421108Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:10:23.1421355Z 2025-03-04T21:10:23.1421777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:10:23.1422790Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:10:23.1423431Z 2025-03-04T21:10:23.1423957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:10:23.1424603Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:10:23.1424890Z 2025-03-04T21:10:23.1425389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:10:23.1426001Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:10:23.1426299Z 2025-03-04T21:10:23.1426733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:10:23.1427249Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:10:23.1427576Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:23.1427917Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:10:23.1428195Z 2025-03-04T21:10:23.1428604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:10:23.1429126Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:10:23.1429452Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:10:23.1429802Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:10:23.1430082Z 2025-03-04T21:10:23.1430492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:10:23.1430993Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:10:23.1431285Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:10:23.1431567Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:10:23.1431827Z 2025-03-04T21:10:23.1432235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:10:23.1432763Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:10:23.1433081Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:10:23.1433376Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:10:23.1433636Z 2025-03-04T21:10:23.1434061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:10:23.1434586Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:10:23.1434926Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:10:23.1435199Z 2025-03-04T21:10:23.1435595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:10:23.1436124Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:10:23.1436447Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:10:23.1436684Z 2025-03-04T21:10:23.1437446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:10:23.1437988Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:10:23.1438318Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:10:23.1438807Z 2025-03-04T21:10:23.1440212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:10:23.1440790Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:10:23.1441146Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:10:23.1441385Z 2025-03-04T21:10:23.1441819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:10:23.1442359Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:10:23.1442620Z 2025-03-04T21:10:23.1443049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:10:23.1443582Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:10:23.1443843Z 2025-03-04T21:10:23.1444277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:10:23.1444823Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:10:23.1445151Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:10:23.1445492Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:10:23.1445844Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:10:23.1446104Z 2025-03-04T21:10:23.1446545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:10:23.1447093Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:10:23.1447417Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:10:23.1447751Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:10:23.1448097Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:10:23.1448622Z 2025-03-04T21:10:23.1449074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:10:23.1449590Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:10:23.1449934Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:10:23.1450321Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:10:23.1450600Z 2025-03-04T21:10:23.1451019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:10:23.1451538Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:10:23.1451896Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:10:23.1452269Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:10:23.1452530Z 2025-03-04T21:10:23.1452949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:10:23.1453451Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:10:23.1453729Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:10:23.1453980Z 2025-03-04T21:10:23.1454390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:10:23.1454957Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:10:23.1455259Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:10:23.1455519Z 2025-03-04T21:10:23.1455958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:10:23.1456488Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:10:23.1456817Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:10:23.1457085Z 2025-03-04T21:10:23.1457511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:10:23.1458013Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:10:23.1458318Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:10:23.1458580Z 2025-03-04T21:10:23.1459039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:10:23.1459656Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:10:23.1459964Z 2025-03-04T21:10:23.1460407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:10:23.1460995Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:10:23.1461300Z 2025-03-04T21:10:23.1461770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:10:23.1462483Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:10:23.1462931Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:10:23.1463231Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:10:23.1463543Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:10:23.1463894Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:10:23.1464161Z 2025-03-04T21:10:23.1464562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:10:23.1465183Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:10:23.1465557Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:10:23.1465840Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:10:23.1466211Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:10:23.1466557Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:10:23.1466801Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:10:23.1467168Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:10:23.1467533Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:10:23.1467775Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:10:23.1468131Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:10:23.1468477Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:10:23.1468718Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:10:23.1468946Z 2025-03-04T21:10:23.1469376Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:10:23.1469980Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:10:23.1470310Z 2025-03-04T21:10:23.1470762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:10:23.1471443Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:10:23.1471882Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:10:23.1472172Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:10:23.1472475Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:10:23.1472798Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:10:23.1473052Z 2025-03-04T21:10:23.1473611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:10:23.1474314Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:10:23.1474656Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:10:23.1474990Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:10:23.1475329Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:10:23.1475621Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:10:23.1475865Z 2025-03-04T21:10:23.1476313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:10:23.1476841Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:10:23.1477080Z 2025-03-04T21:10:23.5571659Z 2025-03-04T21:10:23.5572776Z class GraphModule(torch.nn.Module): 2025-03-04T21:10:23.5573270Z def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"): 2025-03-04T21:10:23.5573939Z l_scores_0_ = L_scores_0_ 2025-03-04T21:10:23.5574163Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:10:23.5574368Z 2025-03-04T21:10:23.5575254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:10:23.5576152Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:10:23.5576506Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:10:23.5576855Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:10:23.5577240Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:10:23.5577539Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:10:23.5577791Z 2025-03-04T21:10:23.5578260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:10:23.5578794Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:10:23.5579036Z 2025-03-04T21:10:23.5579139Z 2025-03-04T21:10:23.5579237Z class GraphModule(torch.nn.Module): 2025-03-04T21:10:23.5579578Z def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"): 2025-03-04T21:10:23.5579907Z l_scores_0_ = L_scores_0_ 2025-03-04T21:10:23.5580110Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:10:23.5580298Z 2025-03-04T21:10:23.5580849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:10:23.5581509Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:10:23.5581827Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:10:23.5582139Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:10:23.5582450Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:10:23.5582737Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:10:23.5582977Z 2025-03-04T21:10:23.5583419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:10:23.5583944Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:10:23.5584182Z 2025-03-04T21:10:37.7782414Z Compilation time (from dynamo_timed): 37.163952727 2025-03-04T21:10:37.7786024Z pass 2025-03-04T21:10:37.7787170Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:10:37.7788316Z TIMING: entire_frame_compile:37.16395 gc:0.04351 _recursive_pre_grad_passes:0.03591 async_compile.wait:3.65969 backend_compile:20.02321 _recursive_joint_graph_passes:0.17409 _recursive_post_grad_passes:0.08081 code_gen:7.26306 inductor_compile:8.64743 total_wall_time:37.16395 2025-03-04T21:10:37.7792517Z STATS: call_* op count: 990 | FakeTensorMode.__torch_dispatch__:22930 | FakeTensor.__torch_dispatch__:1797 | ProxyTorchDispatchMode.__torch_dispatch__:5688 | attempt fast:112 | slow no contiguity match:36 | fast is_contiguous:76 2025-03-04T21:10:37.7793683Z Dynamo produced 61 graphs covering 990 ops with 46 graph breaks (6 unique) 2025-03-04T21:10:43.7942331Z 2025-03-04T21:10:50.0174888Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:10:50.0178033Z loading model: 0it [00:06, ?it/s] 2025-03-04T21:10:50.0187204Z cpu eval detectron2_fcos_r_50_fpn 2025-03-04T21:11:04.7987558Z WARNING:common:fp64 golden ref were not generated for detectron2_fcos_r_50_fpn. Setting accuracy check to cosine 2025-03-04T21:11:04.8029064Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:11:09.3532977Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:11:14.0225481Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:12:11.4298778Z Compilation time (from dynamo_timed): 50.454558661 2025-03-04T21:12:11.4301746Z pass 2025-03-04T21:12:11.4302091Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:12:11.4303186Z TIMING: entire_frame_compile:50.45456 gc:0.02811 _recursive_pre_grad_passes:0.0257 async_compile.wait:15.43044 backend_compile:38.8209 _recursive_joint_graph_passes:0.28142 _recursive_post_grad_passes:0.17934 code_gen:23.3454 inductor_compile:26.59687 total_wall_time:50.45456 2025-03-04T21:12:11.4304106Z STATS: call_* op count: 944 | FakeTensorMode.__torch_dispatch__:29058 | FakeTensor.__torch_dispatch__:3334 | ProxyTorchDispatchMode.__torch_dispatch__:10968 2025-03-04T21:12:11.4304625Z Dynamo produced 29 graphs covering 944 ops with 22 graph breaks (4 unique) 2025-03-04T21:12:16.9354204Z 2025-03-04T21:12:31.0932575Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:12:31.0932892Z loading model: 0it [00:14, ?it/s] 2025-03-04T21:12:31.0933119Z Traceback (most recent call last): 2025-03-04T21:12:31.0933488Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 481, in 2025-03-04T21:12:31.0938870Z torchbench_main() 2025-03-04T21:12:31.0943357Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 477, in torchbench_main 2025-03-04T21:12:31.0943800Z main(TorchBenchmarkRunner(), original_dir) 2025-03-04T21:12:31.0944151Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3489, in main 2025-03-04T21:12:31.0944490Z process_entry(0, runner, original_dir, args) 2025-03-04T21:12:31.0944873Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3421, in process_entry 2025-03-04T21:12:31.0945224Z return run(runner, args, original_dir) 2025-03-04T21:12:31.0945549Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 4074, in run 2025-03-04T21:12:31.0946104Z assert marked, f"nothing in example_inputs had a dim with {batch_size}" 2025-03-04T21:12:31.0946500Z AssertionError: nothing in example_inputs had a dim with 4 2025-03-04T21:12:31.9509842Z Run failed with return code: 1 2025-03-04T21:12:31.9514692Z Output: None 2025-03-04T21:12:31.9516340Z Error: None 2025-03-04T21:12:34.4064076Z 2025-03-04T21:12:42.8407490Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:12:42.8407820Z loading model: 0it [00:08, ?it/s] 2025-03-04T21:12:42.8408053Z Traceback (most recent call last): 2025-03-04T21:12:42.8408430Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 481, in 2025-03-04T21:12:42.8408771Z torchbench_main() 2025-03-04T21:12:42.8409114Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 477, in torchbench_main 2025-03-04T21:12:42.8409503Z main(TorchBenchmarkRunner(), original_dir) 2025-03-04T21:12:42.8409841Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3489, in main 2025-03-04T21:12:42.8410740Z process_entry(0, runner, original_dir, args) 2025-03-04T21:12:42.8415372Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3421, in process_entry 2025-03-04T21:12:42.8418374Z return run(runner, args, original_dir) 2025-03-04T21:12:42.8423408Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 4074, in run 2025-03-04T21:12:42.8425581Z assert marked, f"nothing in example_inputs had a dim with {batch_size}" 2025-03-04T21:12:42.8426313Z AssertionError: nothing in example_inputs had a dim with 4 2025-03-04T21:12:43.6707692Z Run failed with return code: 1 2025-03-04T21:12:43.6708050Z Output: None 2025-03-04T21:12:43.6708265Z Error: None 2025-03-04T21:12:46.1448073Z 2025-03-04T21:13:01.1324428Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:13:01.1325675Z loading model: 0it [00:14, ?it/s] 2025-03-04T21:13:01.1326383Z Traceback (most recent call last): 2025-03-04T21:13:01.1326793Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 481, in 2025-03-04T21:13:01.1327162Z torchbench_main() 2025-03-04T21:13:01.1327509Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 477, in torchbench_main 2025-03-04T21:13:01.1327910Z main(TorchBenchmarkRunner(), original_dir) 2025-03-04T21:13:01.1328345Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3489, in main 2025-03-04T21:13:01.1329898Z process_entry(0, runner, original_dir, args) 2025-03-04T21:13:01.1330592Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3421, in process_entry 2025-03-04T21:13:01.1334137Z return run(runner, args, original_dir) 2025-03-04T21:13:01.1334584Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 4074, in run 2025-03-04T21:13:01.1340164Z assert marked, f"nothing in example_inputs had a dim with {batch_size}" 2025-03-04T21:13:01.1342132Z AssertionError: nothing in example_inputs had a dim with 4 2025-03-04T21:13:01.9797000Z Run failed with return code: 1 2025-03-04T21:13:01.9797307Z Output: None 2025-03-04T21:13:01.9797487Z Error: None 2025-03-04T21:13:04.5087882Z 2025-03-04T21:13:11.5300525Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:13:11.5305296Z loading model: 0it [00:07, ?it/s] 2025-03-04T21:13:11.5310189Z Traceback (most recent call last): 2025-03-04T21:13:11.5315577Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 481, in 2025-03-04T21:13:11.5320488Z torchbench_main() 2025-03-04T21:13:11.5326107Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 477, in torchbench_main 2025-03-04T21:13:11.5326546Z main(TorchBenchmarkRunner(), original_dir) 2025-03-04T21:13:11.5326897Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3489, in main 2025-03-04T21:13:11.5327245Z process_entry(0, runner, original_dir, args) 2025-03-04T21:13:11.5327634Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3421, in process_entry 2025-03-04T21:13:11.5327982Z return run(runner, args, original_dir) 2025-03-04T21:13:11.5328301Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 4074, in run 2025-03-04T21:13:11.5328675Z assert marked, f"nothing in example_inputs had a dim with {batch_size}" 2025-03-04T21:13:11.5329000Z AssertionError: nothing in example_inputs had a dim with 4 2025-03-04T21:13:12.3539679Z Run failed with return code: 1 2025-03-04T21:13:12.3541051Z Output: None 2025-03-04T21:13:12.3541402Z Error: None 2025-03-04T21:13:14.8166306Z 2025-03-04T21:13:20.6575702Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:13:20.6576513Z loading model: 0it [00:05, ?it/s] 2025-03-04T21:13:20.6576773Z cpu eval dlrm 2025-03-04T21:13:21.2233160Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:13:21.4351276Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:13:21.6287166Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:13:31.7722946Z Compilation time (from dynamo_timed): 8.837404868 2025-03-04T21:13:31.7728486Z pass 2025-03-04T21:13:31.7730747Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:13:31.7731971Z TIMING: _recursive_pre_grad_passes:0.00378 _recursive_joint_graph_passes:0.10046 _recursive_post_grad_passes:0.00957 async_compile.wait:1.29919 code_gen:7.61224 inductor_compile:7.73699 backend_compile:8.4787 entire_frame_compile:8.8374 gc:0.00102 total_wall_time:8.8374 2025-03-04T21:13:31.7733145Z STATS: call_* op count: 36 | FakeTensorMode.__torch_dispatch__:1711 | ProxyTorchDispatchMode.__torch_dispatch__:500 | FakeTensor.__torch_dispatch__:81 2025-03-04T21:13:31.7733749Z Dynamo produced 1 graphs covering 36 ops with 0 graph breaks (0 unique) 2025-03-04T21:13:35.9836089Z 2025-03-04T21:13:37.1082108Z loading model: 0it [00:00, ?it/s]Downloading https://doctr-static.mindee.com/models?id=v0.7.0/db_resnet50-79bd7d70.pt&src=0 to /var/lib/jenkins/.cache/doctr/models/db_resnet50-79bd7d70.pt 2025-03-04T21:13:37.5001842Z 2025-03-04T21:13:37.5003636Z 2025-03-04T21:13:37.6003763Z 0% 0/102021912 [00:00=0.7.1 in /usr/lib/python3.9/site-packages (from boto3==1.35.33) (0.10.0) 2025-03-04T21:58:32.2698212Z Collecting s3transfer<0.11.0,>=0.10.0 2025-03-04T21:58:32.2736264Z Downloading s3transfer-0.10.4-py3-none-any.whl (83 kB) 2025-03-04T21:58:33.0289633Z Collecting botocore<1.36.0,>=1.35.33 2025-03-04T21:58:33.0340536Z Downloading botocore-1.35.99-py3-none-any.whl (13.3 MB) 2025-03-04T21:58:33.1598072Z Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (2.8.1) 2025-03-04T21:58:33.1606080Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.25.10) 2025-03-04T21:58:33.2924516Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.15.0) 2025-03-04T21:58:33.3551506Z Installing collected packages: botocore, s3transfer, boto3 2025-03-04T21:58:33.7367355Z Successfully installed boto3-1.35.33 botocore-1.35.99 s3transfer-0.10.4 2025-03-04T21:58:33.8636421Z ##[group]Run set -eux 2025-03-04T21:58:33.8636651Z set -eux 2025-03-04T21:58:33.8636833Z  2025-03-04T21:58:33.8637035Z if [[ -z "${GITHUB_TOKEN}" ]]; then 2025-03-04T21:58:33.8637296Z  echo "Missing github-token input" 2025-03-04T21:58:33.8637525Z  exit 1 2025-03-04T21:58:33.8637700Z fi 2025-03-04T21:58:33.8642473Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:58:33.8642742Z env: 2025-03-04T21:58:33.8642924Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:33.8643237Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:33.8643785Z GITHUB_TOKEN: *** 2025-03-04T21:58:33.8643974Z ##[endgroup] 2025-03-04T21:58:33.8666356Z + [[ -z *** ]] 2025-03-04T21:58:33.8706578Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main 2025-03-04T21:58:33.8706913Z with: 2025-03-04T21:58:33.8707253Z github-token: *** 2025-03-04T21:58:33.8707457Z env: 2025-03-04T21:58:33.8707651Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:33.8708009Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:33.8708374Z ##[endgroup] 2025-03-04T21:58:33.8729843Z ##[group]Run set -eux 2025-03-04T21:58:33.8730076Z set -eux 2025-03-04T21:58:33.8730265Z  2025-03-04T21:58:33.8730799Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-03-04T21:58:33.8735588Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:58:33.8735954Z env: 2025-03-04T21:58:33.8736143Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:33.8736491Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:33.8737244Z GITHUB_TOKEN: *** 2025-03-04T21:58:33.8737451Z ##[endgroup] 2025-03-04T21:58:33.8785404Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/get-workflow-job-id/../../scripts/get_workflow_job_id.py 13661696663 i-07188c9acbdc11b95 2025-03-04T21:58:34.9776292Z setting job-id=38195235058 2025-03-04T21:58:34.9776901Z setting job-name=linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T21:58:34.9875221Z ##[group]Run set -eux 2025-03-04T21:58:34.9875479Z set -eux 2025-03-04T21:58:34.9875656Z  2025-03-04T21:58:34.9875954Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \ 2025-03-04T21:58:34.9876290Z  --schema-version "${SCHEMA_VERSION}" \ 2025-03-04T21:58:34.9876540Z  --repo "${REPO}" \ 2025-03-04T21:58:34.9876760Z  --head-branch "${HEAD_BRANCH}" \ 2025-03-04T21:58:34.9876996Z  --head-sha "${HEAD_SHA}" \ 2025-03-04T21:58:34.9877231Z  --workflow-id "${WORKFLOW_RUN_ID}" \ 2025-03-04T21:58:34.9877476Z  --run-attempt "${RUN_ATTEMPT}" \ 2025-03-04T21:58:34.9877702Z  --job-id "${JOB_ID}" \ 2025-03-04T21:58:34.9877921Z  --job-name "${JOB_NAME}" 2025-03-04T21:58:34.9882078Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:58:34.9882340Z env: 2025-03-04T21:58:34.9882519Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:34.9882865Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:34.9883203Z SCHEMA_VERSION: v3 2025-03-04T21:58:34.9883393Z REPO: pytorch/pytorch 2025-03-04T21:58:34.9883613Z HEAD_BRANCH: refs/tags/ciflow/inductor/148205 2025-03-04T21:58:34.9883870Z HEAD_SHA: 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T21:58:34.9884112Z WORKFLOW_RUN_ID: 13661696663 2025-03-04T21:58:34.9884309Z RUN_ATTEMPT: 1 2025-03-04T21:58:34.9884486Z JOB_ID: 38195235058 2025-03-04T21:58:34.9884842Z JOB_NAME: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T21:58:34.9885208Z ##[endgroup] 2025-03-04T21:58:34.9907232Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_metadata.py --schema-version v3 --repo pytorch/pytorch --head-branch refs/tags/ciflow/inductor/148205 --head-sha 1b7498080987913ecb3aff6253c5e88f3540d911 --workflow-id 13661696663 --run-attempt 1 --job-id 38195235058 --job-name 'linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)' 2025-03-04T21:58:35.0205259Z ##[group]Run set -eux 2025-03-04T21:58:35.0205486Z set -eux 2025-03-04T21:58:35.0205679Z  2025-03-04T21:58:35.0205875Z # TODO (huydhn): Implement this part 2025-03-04T21:58:35.0206126Z echo "runners=[]" >> "${GITHUB_OUTPUT}" 2025-03-04T21:58:35.0210534Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:58:35.0210794Z env: 2025-03-04T21:58:35.0210971Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:35.0211280Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:35.0211603Z ##[endgroup] 2025-03-04T21:58:35.0235253Z + echo 'runners=[]' 2025-03-04T21:58:35.0255904Z ##[group]Run set -eux 2025-03-04T21:58:35.0256136Z set -eux 2025-03-04T21:58:35.0256318Z  2025-03-04T21:58:35.0256530Z # TODO (huydhn): Implement this part 2025-03-04T21:58:35.0256911Z echo "dependencies={}" >> "${GITHUB_OUTPUT}" 2025-03-04T21:58:35.0260824Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:58:35.0261084Z env: 2025-03-04T21:58:35.0261253Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:35.0261621Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:35.0261941Z ##[endgroup] 2025-03-04T21:58:35.0281585Z + echo 'dependencies={}' 2025-03-04T21:58:35.0306284Z ##[group]Run set -eux 2025-03-04T21:58:35.0306513Z set -eux 2025-03-04T21:58:35.0306692Z  2025-03-04T21:58:35.0306902Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then 2025-03-04T21:58:35.0307214Z  echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping" 2025-03-04T21:58:35.0307541Z  # We don't want the job to fail if the directory doesn't exist 2025-03-04T21:58:35.0307807Z  exit 0 2025-03-04T21:58:35.0307983Z fi 2025-03-04T21:58:35.0308151Z  2025-03-04T21:58:35.0308348Z if [[ "${DRY_RUN}" == "true" ]]; then 2025-03-04T21:58:35.0308673Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-03-04T21:58:35.0309040Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-03-04T21:58:35.0309330Z  --metadata "${BENCHMARK_METADATA}" \ 2025-03-04T21:58:35.0309576Z  --runners "${RUNNER_INFO}" \ 2025-03-04T21:58:35.0309827Z  --dependencies "${DEPENDENCIES}" \ 2025-03-04T21:58:35.0310060Z  --dry-run 2025-03-04T21:58:35.0310248Z else 2025-03-04T21:58:35.0310505Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-03-04T21:58:35.0310857Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-03-04T21:58:35.0311141Z  --metadata "${BENCHMARK_METADATA}" \ 2025-03-04T21:58:35.0311385Z  --runners "${RUNNER_INFO}" \ 2025-03-04T21:58:35.0311628Z  --dependencies "${DEPENDENCIES}" 2025-03-04T21:58:35.0311857Z fi 2025-03-04T21:58:35.0315712Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:58:35.0315973Z env: 2025-03-04T21:58:35.0316149Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:35.0316454Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:35.0316788Z BENCHMARK_RESULTS_DIR: test/test-reports 2025-03-04T21:58:35.0317011Z DRY_RUN: false 2025-03-04T21:58:35.0317864Z BENCHMARK_METADATA: {"timestamp": 1741125515, "schema_version": "v3", "name": "linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)", "repo": "pytorch/pytorch", "head_branch": "refs/tags/ciflow/inductor/148205", "head_sha": "1b7498080987913ecb3aff6253c5e88f3540d911", "workflow_id": 13661696663, "run_attempt": 1, "job_id": 38195235058} 2025-03-04T21:58:35.0318734Z RUNNER_INFO: [] 2025-03-04T21:58:35.0318913Z DEPENDENCIES: {} 2025-03-04T21:58:35.0319090Z ##[endgroup] 2025-03-04T21:58:35.0342203Z + [[ ! -d test/test-reports ]] 2025-03-04T21:58:35.0344069Z + [[ false == \t\r\u\e ]] 2025-03-04T21:58:35.1810057Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py --benchmark-results-dir test/test-reports --metadata '{"timestamp": 1741125515, "schema_version": "v3", "name": "linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)", "repo": "pytorch/pytorch", "head_branch": "refs/tags/ciflow/inductor/148205", "head_sha": "1b7498080987913ecb3aff6253c5e88f3540d911", "workflow_id": 13661696663, "run_attempt": 1, "job_id": 38195235058}' --runners '[]' --dependencies '{}' 2025-03-04T21:58:35.1811839Z INFO:root:Upload test/test-reports/inference_torchbench.json to s3://ossci-benchmarks/v3/pytorch/pytorch/13661696663/38195235058/inference_torchbench.json 2025-03-04T21:58:35.2082482Z INFO:botocore.credentials:Found credentials from IAM Role: gh-ci-github-action-runners-runner-role 2025-03-04T21:58:35.4031693Z INFO:root:Upload test/test-reports/inference_torchbench_graph_breaks.json to s3://ossci-benchmarks/v3/pytorch/pytorch/13661696663/38195235058/inference_torchbench_graph_breaks.json 2025-03-04T21:58:35.6075469Z ##[group]Run cat test/**/*_toprint.log || true 2025-03-04T21:58:35.6075754Z cat test/**/*_toprint.log || true 2025-03-04T21:58:35.6080084Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:58:35.6080356Z env: 2025-03-04T21:58:35.6080537Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:35.6080852Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:35.6081170Z ##[endgroup] 2025-03-04T21:58:35.6140173Z cat: 'test/**/*_toprint.log': No such file or directory 2025-03-04T21:58:35.6175249Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2025-03-04T21:58:35.6175530Z kill "$MONITOR_SCRIPT_PID" 2025-03-04T21:58:35.6179670Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:58:35.6179941Z env: 2025-03-04T21:58:35.6180126Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:35.6180448Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:35.6180802Z MONITOR_SCRIPT_PID: 45072 2025-03-04T21:58:35.6181015Z ##[endgroup] 2025-03-04T21:58:35.6286497Z Prepare all required actions 2025-03-04T21:58:35.6286883Z Getting action download info 2025-03-04T21:58:35.7904213Z Download action repository 'actions/upload-artifact@v4' (SHA:4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1) 2025-03-04T21:58:36.1105944Z ##[group]Run ./.github/actions/upload-test-artifacts 2025-03-04T21:58:36.1106202Z with: 2025-03-04T21:58:36.1106488Z file-suffix: test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195235058 2025-03-04T21:58:36.1106810Z s3-bucket: gha-artifacts 2025-03-04T21:58:36.1107007Z env: 2025-03-04T21:58:36.1107177Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:36.1107481Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:36.1107790Z ##[endgroup] 2025-03-04T21:58:36.1130085Z ##[group]Run # Remove any previous test jsons if they exist 2025-03-04T21:58:36.1130416Z # Remove any previous test jsons if they exist 2025-03-04T21:58:36.1130668Z rm -f test-jsons-*.zip 2025-03-04T21:58:36.1130985Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json' 2025-03-04T21:58:36.1135388Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:58:36.1135684Z env: 2025-03-04T21:58:36.1135887Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:36.1136245Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:36.1136747Z FILE_SUFFIX: test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195235058 2025-03-04T21:58:36.1137090Z ##[endgroup] 2025-03-04T21:58:36.1326438Z adding: test/test-reports/inference_torchbench.json (deflated 99%) 2025-03-04T21:58:36.1474735Z adding: test/test-reports/inference_torchbench_graph_breaks.json (deflated 99%) 2025-03-04T21:58:36.1503973Z ##[group]Run # Remove any previous test reports if they exist 2025-03-04T21:58:36.1504322Z # Remove any previous test reports if they exist 2025-03-04T21:58:36.1504590Z rm -f test-reports-*.zip 2025-03-04T21:58:36.1504921Z zip -r "test-reports-${FILE_SUFFIX}.zip" test/test-reports -i '*.xml' -i '*.csv' 2025-03-04T21:58:36.1509107Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:58:36.1509368Z env: 2025-03-04T21:58:36.1509553Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:36.1509866Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:36.1510290Z FILE_SUFFIX: test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195235058 2025-03-04T21:58:36.1510606Z ##[endgroup] 2025-03-04T21:58:36.1551732Z adding: test/test-reports/inference_torchbench.csv (deflated 62%) 2025-03-04T21:58:36.1568524Z adding: test/test-reports/inference_torchbench_graph_breaks.csv (deflated 98%) 2025-03-04T21:58:36.1569149Z adding: test/test-reports/inference_torchbench_graph_break_deduped.csv (deflated 81%) 2025-03-04T21:58:36.1593910Z ##[group]Run # Remove any previous usage logs if they exist 2025-03-04T21:58:36.1594246Z # Remove any previous usage logs if they exist 2025-03-04T21:58:36.1594564Z rm -f logs-*.zip 2025-03-04T21:58:36.1594876Z # this workflow is also run in bazel build test, but we dont generate usage reports for it 2025-03-04T21:58:36.1595260Z # so check to see if the file exists first 2025-03-04T21:58:36.1595507Z if [ -f 'usage_log.txt' ]; then 2025-03-04T21:58:36.1595766Z  zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt' 2025-03-04T21:58:36.1596002Z fi 2025-03-04T21:58:36.1596258Z if find "test/test-reports" -name "*.log" 2>/dev/null | grep -q .; then 2025-03-04T21:58:36.1596602Z  zip -r "logs-${FILE_SUFFIX}.zip" test/test-reports -i '*.log' 2025-03-04T21:58:36.1596858Z fi 2025-03-04T21:58:36.1601357Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:58:36.1601627Z env: 2025-03-04T21:58:36.1601807Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:36.1602257Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:36.1602690Z FILE_SUFFIX: test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195235058 2025-03-04T21:58:36.1603060Z ##[endgroup] 2025-03-04T21:58:36.1674804Z adding: usage_log.txt (deflated 96%) 2025-03-04T21:58:36.1750998Z ##[group]Run # Remove any previous debugging artifacts if they exist 2025-03-04T21:58:36.1751358Z # Remove any previous debugging artifacts if they exist 2025-03-04T21:58:36.1751626Z rm -f debug-*.zip 2025-03-04T21:58:36.1751833Z if [ -d 'test/debug' ]; then 2025-03-04T21:58:36.1752080Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2025-03-04T21:58:36.1752313Z fi 2025-03-04T21:58:36.1756185Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:58:36.1756432Z env: 2025-03-04T21:58:36.1756608Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:36.1756922Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:36.1757354Z FILE_SUFFIX: test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195235058 2025-03-04T21:58:36.1757667Z ##[endgroup] 2025-03-04T21:58:36.1852274Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-03-04T21:58:36.1852545Z with: 2025-03-04T21:58:36.1852747Z s3-bucket: gha-artifacts 2025-03-04T21:58:36.1853013Z s3-prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T21:58:36.1853283Z retention-days: 14 2025-03-04T21:58:36.1853491Z if-no-files-found: warn 2025-03-04T21:58:36.1853743Z path: test-jsons-*.zip 2025-03-04T21:58:36.1853958Z name: artifact 2025-03-04T21:58:36.1854153Z region: us-east-1 2025-03-04T21:58:36.1854345Z env: 2025-03-04T21:58:36.1854530Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:36.1854887Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:36.1855441Z ##[endgroup] 2025-03-04T21:58:36.4825216Z NOTE: s3-prefix specified, ignoring name parameter 2025-03-04T21:58:36.4825575Z With the provided path, there will be 1 file uploaded 2025-03-04T21:58:36.4829448Z Uploading to s3 prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T21:58:36.4849864Z Starting upload of test-jsons-test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195235058.zip 2025-03-04T21:58:36.6173784Z Finished upload of test-jsons-test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195235058.zip 2025-03-04T21:58:36.6324816Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-03-04T21:58:36.6325074Z with: 2025-03-04T21:58:36.6325259Z s3-bucket: gha-artifacts 2025-03-04T21:58:36.6325499Z s3-prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T21:58:36.6325742Z retention-days: 14 2025-03-04T21:58:36.6325929Z if-no-files-found: error 2025-03-04T21:58:36.6326132Z path: test-reports-*.zip 2025-03-04T21:58:36.6326324Z name: artifact 2025-03-04T21:58:36.6326579Z region: us-east-1 2025-03-04T21:58:36.6326754Z env: 2025-03-04T21:58:36.6326912Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:36.6327238Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:36.6327616Z ##[endgroup] 2025-03-04T21:58:36.8929737Z NOTE: s3-prefix specified, ignoring name parameter 2025-03-04T21:58:36.8930282Z With the provided path, there will be 1 file uploaded 2025-03-04T21:58:36.8930737Z Uploading to s3 prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T21:58:36.8954098Z Starting upload of test-reports-test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195235058.zip 2025-03-04T21:58:36.9970452Z Finished upload of test-reports-test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195235058.zip 2025-03-04T21:58:37.0125730Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-03-04T21:58:37.0126006Z with: 2025-03-04T21:58:37.0126198Z s3-bucket: gha-artifacts 2025-03-04T21:58:37.0126451Z s3-prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T21:58:37.0126714Z retention-days: 14 2025-03-04T21:58:37.0126913Z if-no-files-found: ignore 2025-03-04T21:58:37.0127265Z path: logs-*.zip 2025-03-04T21:58:37.0127463Z name: artifact 2025-03-04T21:58:37.0127649Z region: us-east-1 2025-03-04T21:58:37.0127834Z env: 2025-03-04T21:58:37.0128008Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:37.0128334Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:37.0128667Z ##[endgroup] 2025-03-04T21:58:37.2681080Z NOTE: s3-prefix specified, ignoring name parameter 2025-03-04T21:58:37.2681625Z With the provided path, there will be 1 file uploaded 2025-03-04T21:58:37.2682063Z Uploading to s3 prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T21:58:37.2705358Z Starting upload of logs-test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195235058.zip 2025-03-04T21:58:37.3984024Z Finished upload of logs-test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195235058.zip 2025-03-04T21:58:37.4146101Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-03-04T21:58:37.4146358Z with: 2025-03-04T21:58:37.4146570Z s3-bucket: gha-artifacts 2025-03-04T21:58:37.4146821Z s3-prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T21:58:37.4147069Z retention-days: 14 2025-03-04T21:58:37.4147258Z if-no-files-found: ignore 2025-03-04T21:58:37.4147468Z path: debug-*.zip 2025-03-04T21:58:37.4147652Z name: artifact 2025-03-04T21:58:37.4147836Z region: us-east-1 2025-03-04T21:58:37.4148019Z env: 2025-03-04T21:58:37.4148196Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:37.4148511Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:37.4148833Z ##[endgroup] 2025-03-04T21:58:37.6647276Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2025-03-04T21:58:37.6828226Z ##[group]Run # shellcheck disable=SC2156 2025-03-04T21:58:37.6828508Z # shellcheck disable=SC2156 2025-03-04T21:58:37.6828898Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2025-03-04T21:58:37.6834916Z shell: /usr/bin/bash -e {0} 2025-03-04T21:58:37.6835136Z env: 2025-03-04T21:58:37.6835305Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:37.6835618Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:37.6835947Z ##[endgroup] 2025-03-04T21:58:37.8251660Z Prepare all required actions 2025-03-04T21:58:37.8252034Z Getting action download info 2025-03-04T21:58:37.9629362Z ##[group]Run ./.github/actions/upload-utilization-stats 2025-03-04T21:58:37.9629666Z with: 2025-03-04T21:58:37.9629855Z job_id: 38195235058 2025-03-04T21:58:37.9630253Z job_name: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T21:58:37.9631334Z workflow_name: inductor 2025-03-04T21:58:37.9631819Z workflow_run_id: 13661696663 2025-03-04T21:58:37.9632153Z workflow_attempt: 1 2025-03-04T21:58:37.9632570Z env: 2025-03-04T21:58:37.9632769Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:37.9633318Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:37.9633752Z ##[endgroup] 2025-03-04T21:58:37.9655291Z ##[group]Run echo "workflow_id: 13661696663" 2025-03-04T21:58:37.9655604Z echo "workflow_id: 13661696663" 2025-03-04T21:58:37.9655912Z echo "workflow_attempt: 1" 2025-03-04T21:58:37.9656168Z echo "workflow_Name: inductor" 2025-03-04T21:58:37.9656423Z echo "job_id: 38195235058" 2025-03-04T21:58:37.9656828Z echo "job_name: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)" 2025-03-04T21:58:37.9661466Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:58:37.9661730Z env: 2025-03-04T21:58:37.9661913Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:37.9662233Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:37.9662562Z ##[endgroup] 2025-03-04T21:58:37.9686267Z workflow_id: 13661696663 2025-03-04T21:58:37.9686632Z workflow_attempt: 1 2025-03-04T21:58:37.9686875Z workflow_Name: inductor 2025-03-04T21:58:37.9687102Z job_id: 38195235058 2025-03-04T21:58:37.9687565Z job_name: linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T21:58:37.9752306Z ##[group]Run nick-fields/retry@v3.0.0 2025-03-04T21:58:37.9752554Z with: 2025-03-04T21:58:37.9752723Z shell: bash 2025-03-04T21:58:37.9752907Z timeout_minutes: 5 2025-03-04T21:58:37.9753103Z max_attempts: 5 2025-03-04T21:58:37.9753297Z retry_wait_seconds: 30 2025-03-04T21:58:37.9753627Z command: set -eu python3 -m pip install python-dateutil==2.8.2 boto3==1.35.42 pandas==2.1.3 2025-03-04T21:58:37.9753976Z polling_interval_seconds: 1 2025-03-04T21:58:37.9754193Z warning_on_retry: true 2025-03-04T21:58:37.9754393Z continue_on_error: false 2025-03-04T21:58:37.9754589Z env: 2025-03-04T21:58:37.9754755Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:37.9755075Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:37.9755423Z ##[endgroup] 2025-03-04T21:58:38.2549294Z Defaulting to user installation because normal site-packages is not writeable 2025-03-04T21:58:38.3221017Z Collecting python-dateutil==2.8.2 2025-03-04T21:58:38.3405829Z Downloading python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB) 2025-03-04T21:58:38.9734653Z Collecting boto3==1.35.42 2025-03-04T21:58:38.9770587Z Downloading boto3-1.35.42-py3-none-any.whl (139 kB) 2025-03-04T21:58:39.3416620Z Collecting pandas==2.1.3 2025-03-04T21:58:39.3457418Z Downloading pandas-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB) 2025-03-04T21:58:39.4426093Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil==2.8.2) (1.15.0) 2025-03-04T21:58:39.4461762Z Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.42) (0.10.4) 2025-03-04T21:58:39.4464498Z Requirement already satisfied: botocore<1.36.0,>=1.35.42 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.42) (1.35.99) 2025-03-04T21:58:39.4465539Z Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/lib/python3.9/site-packages (from boto3==1.35.42) (0.10.0) 2025-03-04T21:58:40.0325235Z Collecting numpy<2,>=1.22.4 2025-03-04T21:58:40.0366954Z Downloading numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB) 2025-03-04T21:58:40.1507376Z Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3.9/site-packages (from pandas==2.1.3) (2022.7.1) 2025-03-04T21:58:40.1774277Z Collecting tzdata>=2022.1 2025-03-04T21:58:40.1819181Z Downloading tzdata-2025.1-py2.py3-none-any.whl (346 kB) 2025-03-04T21:58:40.1963996Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.42->boto3==1.35.42) (1.25.10) 2025-03-04T21:58:40.3132921Z Installing collected packages: python-dateutil, tzdata, numpy, pandas, boto3 2025-03-04T21:58:44.1480284Z Attempting uninstall: boto3 2025-03-04T21:58:44.1481844Z Found existing installation: boto3 1.35.33 2025-03-04T21:58:44.1546259Z Uninstalling boto3-1.35.33: 2025-03-04T21:58:44.1554937Z Successfully uninstalled boto3-1.35.33 2025-03-04T21:58:44.1974206Z Successfully installed boto3-1.35.42 numpy-1.26.4 pandas-2.1.3 python-dateutil-2.8.2 tzdata-2025.1 2025-03-04T21:58:45.0500681Z Command completed after 1 attempt(s). 2025-03-04T21:58:45.0557184Z ##[group]Run python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \ 2025-03-04T21:58:45.0557634Z python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \ 2025-03-04T21:58:45.0557973Z  --workflow-run-id "13661696663" \ 2025-03-04T21:58:45.0558221Z  --workflow-name "inductor" \ 2025-03-04T21:58:45.0558472Z  --workflow-run-attempt "1" \ 2025-03-04T21:58:45.0558709Z  --job-id "38195235058" \ 2025-03-04T21:58:45.0559104Z  --job-name "linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)" 2025-03-04T21:58:45.0563760Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:58:45.0564024Z env: 2025-03-04T21:58:45.0564223Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:45.0564550Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:45.0564875Z ##[endgroup] 2025-03-04T21:58:46.2144282Z repo: pytorch/pytorch 2025-03-04T21:58:46.2147411Z Downloading logs-test-dynamic_cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195235058.zip 2025-03-04T21:58:46.2147893Z Converted Log Model: UtilizationMetadata: 2025-03-04T21:58:46.2148873Z UtilizationMetadata(level='metadata', workflow_id='13661696663', job_id='38195235058', workflow_name='inductor', job_name='linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)', usage_collect_interval=1.0, data_model_version=1.0, start_at=1741119429, gpu_count=0, cpu_count=32, gpu_type='', error=None) 2025-03-04T21:58:46.2149918Z [Db Segments] detected pytest cmd: 15, generated segments: 15 2025-03-04T21:58:46.2150245Z [db model] Peek db timeseries 2025-03-04T21:58:46.2150494Z :{ 2025-03-04T21:58:46.2150714Z "created_at": 1741125525, 2025-03-04T21:58:46.2150983Z "type": "utilization", 2025-03-04T21:58:46.2151206Z "tags": [ 2025-03-04T21:58:46.2151411Z "record" 2025-03-04T21:58:46.2151623Z ], 2025-03-04T21:58:46.2151800Z "time_stamp": 1741119429, 2025-03-04T21:58:46.2152013Z "repo": "pytorch/pytorch", 2025-03-04T21:58:46.2152225Z "workflow_id": 13661696663, 2025-03-04T21:58:46.2152444Z "run_attempt": 1, 2025-03-04T21:58:46.2152629Z "job_id": 38195235058, 2025-03-04T21:58:46.2152829Z "workflow_name": "inductor", 2025-03-04T21:58:46.2153207Z "job_name": "linux-jammy-cpu-py3.9-gcc11-inductor / test (dynamic_cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)", 2025-03-04T21:58:46.2153577Z "json_data": "{}" 2025-03-04T21:58:46.2153760Z } 2025-03-04T21:58:46.2154087Z Writing 1 documents to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/13661696663/1/38195235058/metadata 2025-03-04T21:58:46.2155031Z Done! Finish writing document to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/13661696663/1/38195235058/metadata 2025-03-04T21:58:46.2155590Z Writing 1207 documents to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/13661696663/1/38195235058/time_series 2025-03-04T21:58:46.2156154Z Done! Finish writing document to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/13661696663/1/38195235058/time_series 2025-03-04T21:58:46.3731392Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main 2025-03-04T21:58:46.3731719Z with: 2025-03-04T21:58:46.3731907Z env: 2025-03-04T21:58:46.3732122Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:46.3732549Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:46.3732896Z ##[endgroup] 2025-03-04T21:58:46.3753774Z ##[group]Run set -eou pipefail 2025-03-04T21:58:46.3754060Z set -eou pipefail 2025-03-04T21:58:46.3754282Z  2025-03-04T21:58:46.3754572Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2025-03-04T21:58:46.3754908Z for _ in $(seq 1440); do 2025-03-04T21:58:46.3755174Z  # Break if no ssh session exists anymore 2025-03-04T21:58:46.3755443Z  if [ "$(who)" = "" ]; then 2025-03-04T21:58:46.3755678Z  break 2025-03-04T21:58:46.3755871Z  fi 2025-03-04T21:58:46.3756104Z  echo "." 2025-03-04T21:58:46.3756307Z  sleep 5 2025-03-04T21:58:46.3756504Z done 2025-03-04T21:58:46.3760963Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:58:46.3761251Z env: 2025-03-04T21:58:46.3761444Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:46.3761780Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:46.3762140Z ##[endgroup] 2025-03-04T21:58:46.3782668Z Holding runner for 2 hours until all ssh sessions have logged out 2025-03-04T21:58:46.3874237Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-03-04T21:58:46.3874642Z # ignore expansion of "docker ps -q" since it could be empty 2025-03-04T21:58:46.3874933Z # shellcheck disable=SC2046 2025-03-04T21:58:46.3875177Z docker stop $(docker ps -q) || true 2025-03-04T21:58:46.3875420Z # Prune all of the docker images 2025-03-04T21:58:46.3875651Z docker system prune -af 2025-03-04T21:58:46.3879860Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:58:46.3880125Z env: 2025-03-04T21:58:46.3880307Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:58:46.3880629Z DOCKER_CONTAINER_ID: 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:46.3880955Z ##[endgroup] 2025-03-04T21:58:57.3140605Z 3688f39b3e9f 2025-03-04T21:58:58.5326346Z Deleted Containers: 2025-03-04T21:58:58.5326756Z 3688f39b3e9faf01ccad6e6548a20712946b010b0ae81598c5ca4dfffa3773bb 2025-03-04T21:58:58.5326995Z 2025-03-04T21:59:02.0278308Z Deleted Images: 2025-03-04T21:59:02.0279091Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:59:02.0280053Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks@sha256:bd007eb2fe9e7d1c860264514799356825b802ed452803de01a654c76280cd51 2025-03-04T21:59:02.0280848Z 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safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-03-04T21:59:02.1655235Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-03-04T21:59:02.1689337Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :" 2025-03-04T21:59:02.2000862Z Entering 'android/libs/fbjni' 2025-03-04T21:59:02.2051110Z Entering 'third_party/FP16' 2025-03-04T21:59:02.2111817Z Entering 'third_party/FXdiv' 2025-03-04T21:59:02.2158417Z Entering 'third_party/NNPACK' 2025-03-04T21:59:02.2205022Z Entering 'third_party/NVTX' 2025-03-04T21:59:02.2259710Z Entering 'third_party/VulkanMemoryAllocator' 2025-03-04T21:59:02.2306417Z Entering 'third_party/XNNPACK' 2025-03-04T21:59:02.2369156Z Entering 'third_party/benchmark' 2025-03-04T21:59:02.2551185Z Entering 'third_party/composable_kernel' 2025-03-04T21:59:02.2615787Z Entering 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2025-03-04T21:59:02.7697791Z http.https://github.com/.extraheader 2025-03-04T21:59:02.7761624Z Entering 'third_party/fbgemm/third_party/cutlass' 2025-03-04T21:59:02.7796976Z http.https://github.com/.extraheader 2025-03-04T21:59:02.7836807Z Entering 'third_party/fbgemm/third_party/googletest' 2025-03-04T21:59:02.7869046Z http.https://github.com/.extraheader 2025-03-04T21:59:02.7902654Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2025-03-04T21:59:02.7935331Z http.https://github.com/.extraheader 2025-03-04T21:59:02.7970695Z Entering 'third_party/flash-attention' 2025-03-04T21:59:02.8003755Z http.https://github.com/.extraheader 2025-03-04T21:59:02.8039065Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-03-04T21:59:02.8074828Z http.https://github.com/.extraheader 2025-03-04T21:59:02.8114443Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-03-04T21:59:02.8159445Z http.https://github.com/.extraheader 2025-03-04T21:59:02.8204298Z Entering 'third_party/flatbuffers' 2025-03-04T21:59:02.8235577Z http.https://github.com/.extraheader 2025-03-04T21:59:02.8274647Z Entering 'third_party/fmt' 2025-03-04T21:59:02.8307480Z http.https://github.com/.extraheader 2025-03-04T21:59:02.8345076Z Entering 'third_party/gemmlowp/gemmlowp' 2025-03-04T21:59:02.8378701Z http.https://github.com/.extraheader 2025-03-04T21:59:02.8415166Z Entering 'third_party/gloo' 2025-03-04T21:59:02.8447401Z http.https://github.com/.extraheader 2025-03-04T21:59:02.8481024Z Entering 'third_party/googletest' 2025-03-04T21:59:02.8513247Z http.https://github.com/.extraheader 2025-03-04T21:59:02.8549886Z Entering 'third_party/ideep' 2025-03-04T21:59:02.8580752Z http.https://github.com/.extraheader 2025-03-04T21:59:02.8613196Z Entering 'third_party/ideep/mkl-dnn' 2025-03-04T21:59:02.8645852Z http.https://github.com/.extraheader 2025-03-04T21:59:02.8732955Z Entering 'third_party/ittapi' 2025-03-04T21:59:02.8766625Z http.https://github.com/.extraheader 2025-03-04T21:59:02.8813444Z Entering 'third_party/kineto' 2025-03-04T21:59:02.8844025Z http.https://github.com/.extraheader 2025-03-04T21:59:02.8878161Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-03-04T21:59:02.8913458Z http.https://github.com/.extraheader 2025-03-04T21:59:02.8945884Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-03-04T21:59:02.8977942Z http.https://github.com/.extraheader 2025-03-04T21:59:02.9013586Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-03-04T21:59:02.9045731Z http.https://github.com/.extraheader 2025-03-04T21:59:02.9081144Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-03-04T21:59:02.9112802Z http.https://github.com/.extraheader 2025-03-04T21:59:02.9146744Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-03-04T21:59:02.9173992Z http.https://github.com/.extraheader 2025-03-04T21:59:02.9219076Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-03-04T21:59:02.9249433Z http.https://github.com/.extraheader 2025-03-04T21:59:02.9289900Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-03-04T21:59:02.9330845Z http.https://github.com/.extraheader 2025-03-04T21:59:02.9353642Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-03-04T21:59:02.9384142Z http.https://github.com/.extraheader 2025-03-04T21:59:02.9421516Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-03-04T21:59:02.9450344Z http.https://github.com/.extraheader 2025-03-04T21:59:02.9494843Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-03-04T21:59:02.9522541Z http.https://github.com/.extraheader 2025-03-04T21:59:02.9555462Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-03-04T21:59:02.9585560Z http.https://github.com/.extraheader 2025-03-04T21:59:02.9622638Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-03-04T21:59:02.9654902Z http.https://github.com/.extraheader 2025-03-04T21:59:02.9701113Z Entering 'third_party/kleidiai' 2025-03-04T21:59:02.9732327Z http.https://github.com/.extraheader 2025-03-04T21:59:02.9765382Z Entering 'third_party/mimalloc' 2025-03-04T21:59:02.9799321Z http.https://github.com/.extraheader 2025-03-04T21:59:02.9833159Z Entering 'third_party/nlohmann' 2025-03-04T21:59:02.9867199Z http.https://github.com/.extraheader 2025-03-04T21:59:02.9898682Z Entering 'third_party/onnx' 2025-03-04T21:59:02.9928994Z http.https://github.com/.extraheader 2025-03-04T21:59:02.9976632Z Entering 'third_party/onnx/third_party/pybind11' 2025-03-04T21:59:03.0008002Z http.https://github.com/.extraheader 2025-03-04T21:59:03.0048789Z Entering 'third_party/opentelemetry-cpp' 2025-03-04T21:59:03.0082188Z http.https://github.com/.extraheader 2025-03-04T21:59:03.0126306Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-03-04T21:59:03.0147365Z http.https://github.com/.extraheader 2025-03-04T21:59:03.0180080Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-03-04T21:59:03.0210363Z http.https://github.com/.extraheader 2025-03-04T21:59:03.0247953Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-03-04T21:59:03.0275355Z http.https://github.com/.extraheader 2025-03-04T21:59:03.0314271Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-03-04T21:59:03.0348309Z http.https://github.com/.extraheader 2025-03-04T21:59:03.0387113Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-03-04T21:59:03.0411663Z http.https://github.com/.extraheader 2025-03-04T21:59:03.0447529Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-03-04T21:59:03.0482719Z http.https://github.com/.extraheader 2025-03-04T21:59:03.0554825Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-03-04T21:59:03.0583669Z http.https://github.com/.extraheader 2025-03-04T21:59:03.0617761Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-03-04T21:59:03.0649240Z http.https://github.com/.extraheader 2025-03-04T21:59:03.0685698Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-03-04T21:59:03.0711361Z http.https://github.com/.extraheader 2025-03-04T21:59:03.0809124Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-03-04T21:59:03.0839707Z http.https://github.com/.extraheader 2025-03-04T21:59:03.0891637Z Entering 'third_party/pocketfft' 2025-03-04T21:59:03.0925071Z http.https://github.com/.extraheader 2025-03-04T21:59:03.0959043Z Entering 'third_party/protobuf' 2025-03-04T21:59:03.0993074Z http.https://github.com/.extraheader 2025-03-04T21:59:03.1027677Z Entering 'third_party/protobuf/third_party/benchmark' 2025-03-04T21:59:03.1056205Z http.https://github.com/.extraheader 2025-03-04T21:59:03.1094036Z Entering 'third_party/protobuf/third_party/googletest' 2025-03-04T21:59:03.1126868Z http.https://github.com/.extraheader 2025-03-04T21:59:03.1164767Z Entering 'third_party/psimd' 2025-03-04T21:59:03.1199481Z http.https://github.com/.extraheader 2025-03-04T21:59:03.1233221Z Entering 'third_party/pthreadpool' 2025-03-04T21:59:03.1264469Z http.https://github.com/.extraheader 2025-03-04T21:59:03.1307549Z Entering 'third_party/pybind11' 2025-03-04T21:59:03.1341647Z http.https://github.com/.extraheader 2025-03-04T21:59:03.1424759Z Entering 'third_party/python-peachpy' 2025-03-04T21:59:03.1459315Z http.https://github.com/.extraheader 2025-03-04T21:59:03.1494967Z Entering 'third_party/sleef' 2025-03-04T21:59:03.1524051Z http.https://github.com/.extraheader 2025-03-04T21:59:03.1557700Z Entering 'third_party/tensorpipe' 2025-03-04T21:59:03.1588385Z http.https://github.com/.extraheader 2025-03-04T21:59:03.1620858Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-03-04T21:59:03.1651029Z http.https://github.com/.extraheader 2025-03-04T21:59:03.1683666Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-03-04T21:59:03.1718817Z http.https://github.com/.extraheader 2025-03-04T21:59:03.1752430Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-03-04T21:59:03.1784948Z http.https://github.com/.extraheader 2025-03-04T21:59:03.1841397Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-03-04T21:59:03.1856772Z http.https://github.com/.extraheader 2025-03-04T21:59:03.1888576Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-03-04T21:59:03.1922580Z http.https://github.com/.extraheader 2025-03-04T21:59:03.2062065Z A job completed hook has been configured by the self-hosted runner administrator 2025-03-04T21:59:03.2080542Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2025-03-04T21:59:03.2083587Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:59:03.2083859Z ##[endgroup] 2025-03-04T21:59:03.2160119Z [!ALERT!] Swap in detected! [!ALERT!] 2025-03-04T21:59:12.1991991Z [!ALERT!] Swap out detected [!ALERT!] 2025-03-04T21:59:26.2057520Z Cleaning up orphan processes